Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS)


 

About

The “Synthetic Perceptive, Emotive and Cognitive Systems (SPECS)” group led by Prof. Paul Verschure (http://specs-lab.com/) is a multidisciplinary research group** interested in understanding the neuronal, psychological and behavioural principles underlying perception, emotion and cognition. Please check http://specs-lab.com/what_we_do/#Understand

SPECS mission is to understand mind, brain and behaviour through constructing synthetic perceptive, emotive and cognitive systems. SPECS’s interdisciplinary research has a long-standing tradition in the domains of computational neuroscience, cognitive science and interactive systems with a focus on neuro-technologies, robotics and AI, combining real word systems such as large-scale interactive systems, robots and virtual reality systems to understand build and repair the brain and the body.

BrainX3 visualized in the Experience Induction Machines (XIM by SPECS-lab)
BrainX3 is an interactive platform for 3D visualization, analysis and simulation of human neuroimaging data and brain models.

Following a deductive medicine, approach SPECS is investigating a range of brain pathologies where medical interventions are explicit validations of scientific predictions.

A humanoid Robot (Pepper) interacts with a researcher from SPECS-lab to test the brain-based Cognitive Architecture DAC (Verschure et al. 1999-2016) and explore Human-Robot Collaboration.

The cornerstone of SPECS’s work on the biological principles underlying perception, cognition and behaviour is provided by the Distributed Adaptive Control (DAC) architecture. The DAC architecture is built on a series of neuronal models of learning and problem solving, developed over the last 15 years and constitutes an evolving candidate theory of general intelligence and cognition. DAC has become a standard in the field of artificial intelligence and behavior based robotics. DAC can account for aspects of human cognition that are usually only addressed with top-down symbolic models.

 

 

**The group includes 1 Professor, 1 senior scientist, 5 postdocs, 6 PhD students, 2 Research assistants, 6 Technical Support to Research, 1 administration support. SPECS also hosts several master students, international students, undergraduate students.

 

SPECS is also involved in the following activities:

  • Master program: Cognitive Systems and Interactive Media at UPF
  • Summer school: Barcelona Cognition Brain and Technology
  • Conference: LIVING MACHINES conference
  • Summer courses:  NEURO-ROBOTICS for children (BIYSC)
  • Dissemination: Outreach Activities  Escolab –  Semana Cerebro –  Festa de la Ciencia  – Bojos…

Staff

 

Paul Verschure | Group Leader / ICREA Research Professor
Anna Mura | Senior Researcher
Daniel Pacheco | Postdoctoral Researcher
Diogo Pata Santos | Postdoctoral Researcher
Belén Rubio Ballester | Postdoctoral Researcher
Pedro Omedas Morera | Senior Technical Support to Research
Patricia Fuentes Abadia | Administrative Assistant
Adrián Fernández Amil | PhD Student
Ismael Tito Freire González | PhD Student
Oscar Guerrero Rosado | PhD Student
Héctor López Carral | PhD Student
Vivek Sharma | Research Assistant
Alejandro Escuredo Chimeno | Technical Support to Research
Enrique Martínez Bueno | Technical Support to Research
Martin Alexandre Alonso Iglesias | Masters Student
Katherine Robles Martínez de le Vega | Masters Student
Marco Galli | Visiting Researcher

 

 


Projects

EU-funded projects
RGS@HOME Scaling ICT based neurorehabilitation to personalized 24/7 home care (2019-2021) EIT Health Paul Verschure
ANITA Advanced tools for fighting oNline Illegal TrAfficking (2018-2021) European Comission Research and Innovation action Paul Verschure
VirtualBrainCloud Personalized Recommendations for Neurodegenerative Disease (2018-2022) European Comission Digital transformation in Health and Care Paul Verschure
HR-Recycler Hybrid Human-Robot RECYcling plant for electriCal and eLEctRonic equipment (2018-2022) European Comission Transforming European Industry Paul Verschure
iNavigate Brain-inspired technologies for intelligent navigation and mobility (2019-2023) European Comission Marie Curie Paul Verschure
cRGS Cognitive Rehabilitation Gamming System (cRGS): a novel Virtual Reality-based system for the conjunctive training of stroke-derived cognitive impairments (2019-2021) European Comission ERC – PoC Paul Verschure
ReHyb Rehabilitation based on Hybrid neuroprosthesis (2020-2023) European Comission Paul Verschure

EIT Health is supported by EIT, a body of the European Union

 

 

National projects
TECSAM · Xarxa d’Innovació de Noves Tecnologies en Salut Mental AGAUR Paul Verschure
Finished Projects
HBP Education Activity (2020) European Comission FET Flaships Paul Verschure
DAC-CHM Distributed Adaptive Control of Consciousness in Humans and Machines (2017-2020) MINECO
Retos investigación: Proyectos I+D
Paul Verschure
socSMCs Socialising Sensori-Motor Contingencies (2017-2020) European Comission FET Proactive Paul Verschure
CDAC  The role of consciousness in adaptive behavior: A combined empirical, computational and robot-based approach (2014-2020) ERC Advanced Grant Paul Verschure
iC-ACCESS  Accessing Campscapes: Inclusive Strategies for Using European Conflicted Heritage (2016-2019) HERA Joint Research Programme Uses of the Past, REFLECTIVE-1-2014 Paul Verschure
socSMCs  Socialising Sensori-Motor Contingencies (2015-2018) Future Emerging Technologies (FET), H2020 Paul Verschure
WYSIWYD  What You Say Is What You Did (2014-2017) FP7-ICT-2013-10, grant agreement n° 612139 Paul Verschure
EASEL  Expressive Agents for Symbiotic Education and Learning (2013-2017) FP7-ICT-2013-10, grant agreement n° 611971 Paul Verschure
CSNII  Convergent Science Network of Biomimetics and Neurotechnology (2013-2016) FP7-ICT-601167 Paul Verschure
INSOCO (2015-2018) Paul Verschure
SANaR Smart Autonomous Neuro-Rehabilitation System MINECO, Retos Investigación 2013 Paul Verschure
TECNIO (2016-2019) Generalitat of Catalonia Paul Verschure

 

Publications

Low, S. C., Vouloutsi, V., Verschure, P., (2021). Complementary interactions between classical and top-down driven inhibitory mechanisms of attention Cognitive Systems Research 67, 66-72

Selective attention informs decision-making by biasing perceptual processing towards task-relevant stimuli. In experimental and computational literature, this is most often implemented through top-down excitation of selected stimuli. However, physiological and anatomical evidence shows that in certain situations, top-down signals could instead be inhibitory. In this study, we investigated how such an inhibitory mechanism of top-down attention compares with an excitatory one. We did so in a neurorobotics context where the agent was controlled using an established hierarchical architecture. We augmented the architecture with an attentional system that implemented top-down attention biasing as connection gains. We tested four models of top-down attention on the simulated agent performing a foraging task: without top-down biasing, with only excitatory top-down gain, with only inhibitory top-down gain, and with both excitatory and inhibitory top-down gain. We manipulated the reward-distractor ratio that was presented and assessed the agent's performance using accumulated rewards and the latency of the selection. Using these measures, we provide evidence that excitatory and inhibitory mechanisms of attention complement each other.

Keywords: Selective attention, Inhibition, Foraging, Embodied cognition


Wiers, Reinout W., Verschure, Paul, (2021). Curing the broken brain model of addiction: Neurorehabilitation from a systems perspective Addictive Behaviors 112, 106602

The dominant biomedical perspective on addictions has been that they are chronic brain diseases. While we acknowledge that the brains of people with addictions differ from those without, we argue that the “broken brain” model of addiction has important limitations. We propose that a systems-level perspective more effectively captures the integrated architecture of the embodied and situated human mind and brain in relation to the development of addictions. This more dynamic conceptualization places addiction in the broader context of the addicted brain that drives behavior, where the addicted brain is the substrate of the addicted mind, that in turn is situated in a physical and socio-cultural environment. From this perspective, neurorehabilitation should shift from a “broken-brain” to a systems theoretical framework, which includes high-level concepts related to the physical and social environment, motivation, self-image, and the meaning of alternative activities, which in turn will dynamically influence subsequent brain adaptations. We call this integrated approach system-oriented neurorehabilitation. We illustrate our proposal by showing the link between addiction and the architecture of the embodied brain, including a systems-level perspective on classical conditioning, which has been successfully translated into neurorehabilitation. Central to this example is the notion that the human brain makes predictions on future states as well as expected (or counterfactual) errors, in the context of its goals. We advocate system-oriented neurorehabilitation of addiction where the patients' goals are central in targeted, personalized assessment and intervention.

Keywords: Addiction, Brain disease model, Neurorehabilitation, Systems approach


Pacheco Estefan, D., Zucca, Riccardo, Arsiwalla, X. D., Principe, A., Zhang, Hui, Rocamora, R., Axmacher, N., Verschure, P., (2021). Volitional learning promotes theta phase coding in the human hippocampus Proceedings of the National Academy of Sciences of the United States of America 118, (10), e2021238118

Electrophysiological studies in rodents show that active navigation enhances hippocampal theta oscillations (4–12 Hz), providing a temporal framework for stimulus-related neural codes. Here we show that active learning promotes a similar phase coding regime in humans, although in a lower frequency range (3–8 Hz). We analyzed intracranial electroencephalography (iEEG) from epilepsy patients who studied images under either volitional or passive learning conditions. Active learning increased memory performance and hippocampal theta oscillations and promoted a more accurate reactivation of stimulus-specific information during memory retrieval. Representational signals were clustered to opposite phases of the theta cycle during encoding and retrieval. Critically, during active but not passive learning, the temporal structure of intracycle reactivations in theta reflected the semantic similarity of stimuli, segregating conceptually similar items into more distant theta phases. Taken together, these results demonstrate a multilayered mechanism by which active learning improves memory via a phylogenetically old phase coding scheme.

Keywords: Active learning, Intracranial EEG, Theta oscillations, Neural phase coding, Hippocampus


Blancas, Maria, Valero, Cristina, Vouloutsi, Vasiliki, Mura, Anna, Verschure, P., (2021). Educational robotics: A journey, not a destination Handbook of Research on Using Educational Robotics to Facilitate Student Learning (ed. Papadakis, Stamatios, Kalogiannakis, Michail), IGI Global (Hershey, PA, USA) , 41-67

The aim of this work is two-fold. On the one hand, the authors wish to provide relevant information to educators willing to develop an educational robotics (ER) curriculum. They thus provide the current state of the art in the field of ER and the various approaches reported in the literature. They also provide examples of how computational thinking (CT) can be applied in ER and main theories behind ER: constructivism, constructionism, and inquiry-based learning. As ER requires problem-solving abilities, they discuss the link between CT and metacognition, which is considered one of the required educational improvements of the 21st century (also related to the role of gender in STEM methodologies). On the other hand, they wish to present their methodology to teach coding and ER (coding robots through exploring their affordances – CREA), how it was designed, and its main outcomes. It aims at teaching programming and robotics to children in primary school, focusing not on only the performance of the students, but also the cultivation of collaboration, communication, creativity, and critical thinking.


Maier, Martina, Ballester, Belén Rubio, Leiva Bañuelos, Nuria, Duarte Oller, Esther, Verschure, P., (2020). Adaptive conjunctive cognitive training (ACCT) in virtual reality for chronic stroke patients: a randomized controlled pilot trial Journal of NeuroEngineering and Rehabilitation 17, (1), 42

Current evidence for the effectiveness of post-stroke cognitive rehabilitation is weak, possibly due to two reasons. First, patients typically express cognitive deficits in several domains. Therapies focusing on specific cognitive deficits might not address their interrelated neurological nature. Second, co-occurring psychological problems are often neglected or not diagnosed, although post-stroke depression is common and related to cognitive deficits. This pilot trial aims to test a rehabilitation program in virtual reality that trains various cognitive domains in conjunction, by adapting to the patient’s disability and while investigating the influence of comorbidities.


Blancas, Maria, Maffei, Giovanni, Sánchez-Fibla, Martí, Vouloutsi, Vasiliki, Verschure, P., (2020). Collaboration variability in autism spectrum disorder Frontiers in Human Neuroscience 14, (412), 559793

This paper addresses how impairments in prediction in young adults with autism spectrum disorder (ASD) relate to their behavior during collaboration. To assess it, we developed a task where participants play in collaboration with a synthetic agent to maximize their score. The agent’s behavior changes during the different phases of the game, requiring participants to model the agent’s sensorimotor contingencies to play collaboratively. Our results (n = 30, 15 per group) show differences between autistic and neurotypical individuals in their behavioral adaptation to the other partner. Contrarily, there are no differences in the self-reports of that collaboration.

Keywords: Autism, Prediction, Collaboration, Sensorimotor contingencies, Neurodiversity


Verschure, P., (2020). The distributed adaptive control theory of the mind and brain as a candidate standard model of the human mind Common Model of Cognition Bulletin 1, (2 - A Standard Model of the Mind), 481-486

This article presents the Distributed Adaptive Control (DAC) theory of mind and brain as a candidate standard model of the human mind. DAC is defined against a reformulation of the criteria for unified theories of cognition advanced by Allen Newell, or the Unified Theories of Embodied Minds – Standard Model benchmark (UTEM-SM) that emphasizes real-world and real-time embodied action. DAC considers mind and brain as the function and implementation of a multi-layered control system and addresses the fundamental question of how the mind, as the product of embodied and situated brains, can obtain, retain and express valid knowledge of its world and transform this into policies for action. DAC provides an explanatory framework for biological minds and brains by satisfying well-defined constraints faced by theories of mind and brain and provides a route for the convergent validation of anatomy, physiology, and behavior in our explanation of biological minds. DAC is a well validated integration and synthesis framework for artificial minds and exemplifies the role of the synthetic method in understanding mind and brain. This article describes the core components of DAC, its performance on specific benchmarks derived from the engagement with the physical and the social world (or the H4W and the H5W problems) and lastly analyzes DAC’s performance on the UTEM-SM benchmark and its relationship with contemporary developments in AI.


Sánchez-Fibla, M., Forestier, S., Moulin-Frier, C., Puigbò, J. Y., Verschure, P., (2020). From motor to visually guided bimanual affordance learning Adaptive Behavior 28, (2), 63-78

The mechanisms of how the brain orchestrates multi-limb joint action have yet to be elucidated and few computational sensorimotor (SM) learning approaches have dealt with the problem of acquiring bimanual affordances. We propose a series of bidirectional (forward/inverse) SM maps and its associated learning processes that generalize from uni- to bimanual interaction (and affordances) naturally, reinforcing the motor equivalence property. The SM maps range from a SM nature to a solely sensory one: full body control, delta SM control (through small action changes), delta sensory co-variation (how body-related perceptual cues covariate with object-related ones). We make several contributions on how these SM maps are learned: (1) Context and Behavior-Based Babbling: generalizing goal babbling to the interleaving of absolute and local goals including guidance of reflexive behaviors; (2) Event-Based Learning: learning steps are driven by visual, haptic events; and (3) Affordance Gradients: the vectorial field gradients in which an object can be manipulated. Our modeling of bimanual affordances is in line with current robotic research in forward visuomotor mappings and visual servoing, enforces the motor equivalence property, and is also consistent with neurophysiological findings like the multiplicative encoding scheme.

Keywords: Affordances, Bimanual affordances, Goal babbling, Interlimb coordination, Motor equivalence, Sensorimotor learning


Bortolla, R., Cavicchioli, M., Soler Rivaldi, J., Pascual Mateos, J.C., Verschure, P., Maffei, C., (2020). Hypersensitivity or hyperreactivity? An experimental investigation in Borderline Personality Disorder Mediterranean Journal of Clinical Psychology 8, (1), 1-17

Objective: Starting from the controversial results showed by empirical research on Linehan’s Biosocial model of Borderline Personality Disorder (BPD), this study aims to empirically evaluate Linehan’s conceptualization of emotional hypersensitivity and hyperreactivity, as well as to investigate the role of pre-existing emotional states in BPD altered physiological responsivity. Methods: We asked 24 participants (BPD = 12; Healthy Controls = 12) to complete a self-reported questionnaire (Positive and Negative Affect Schedule) in order to assess their pre-task affective state. Subsequently, 36 emotional pictures from four valence categories (i.e. erotic, negative, positive, neutral) were administered while assessing participants self-reported and electrodermal responses. Results: BPD patients showed higher levels of pre-task negative affectivity as well as an enhanced physiological response to neutral stimuli. No main BPD group effect was found for the physiological data. Moreover, pre-task negative affectivity levels were exclusively related to physiological responses among BPD subjects. Discussion: Our findings supported the hypersensitivity hypothesis operationalized as an enhanced responsiveness to non-emotional cues. Hyperreactivity assumption was not supported. Conversely, our study revealed heightened physiological responses in relation to pre-existent negative emotional states in BPD. We discussed our results in the context of the putative pathological processes underlying BPD.

Keywords: Borderline Personality Disorder, Biosocial model, Hyperreactivity, Hypersensitivity, Negative affectivity, Physiology.


Freire, Ismael T., Moulin-Frier, Clement, Sanchez-Fibla, Marti, Arsiwalla, Xerxes D., Verschure, P., (2020). Modeling the formation of social conventions from embodied real-time interactions PLoS ONE PLOS ONE , 15, (6), e0234434

What is the role of real-time control and learning in the formation of social conventions? To answer this question, we propose a computational model that matches human behavioral data in a social decision-making game that was analyzed both in discrete-time and continuous-time setups. Furthermore, unlike previous approaches, our model takes into account the role of sensorimotor control loops in embodied decision-making scenarios. For this purpose, we introduce the Control-based Reinforcement Learning (CRL) model. CRL is grounded in the Distributed Adaptive Control (DAC) theory of mind and brain, where low-level sensorimotor control is modulated through perceptual and behavioral learning in a layered structure. CRL follows these principles by implementing a feedback control loop handling the agent’s reactive behaviors (pre-wired reflexes), along with an Adaptive Layer that uses reinforcement learning to maximize long-term reward. We test our model in a multi-agent game-theoretic task in which coordination must be achieved to find an optimal solution. We show that CRL is able to reach human-level performance on standard game-theoretic metrics such as efficiency in acquiring rewards and fairness in reward distribution.


Grechuta, Klaudia, Rubio Ballester, Belén, Espín Munné, Rosa, Usabiaga Bernal, Teresa, Molina Hervás, Begoña, Mohr, Bettina, Pulvermüller, Friedemann, San Segundo, Rosa Maria, Verschure, P., (2020). Multisensory cueing facilitates naming in aphasia Journal of NeuroEngineering and Rehabilitation 17, (1), 122

Impaired naming is a ubiquitous symptom in all types of aphasia, which often adversely impacts independence, quality of life, and recovery of affected individuals. Previous research has demonstrated that naming can be facilitated by phonological and semantic cueing strategies that are largely incorporated into the treatment of anomic disturbances. Beneficial effects of cueing, whereby naming becomes faster and more accurate, are often attributed to the priming mechanisms occurring within the distributed language network.


Vodovotz, Y., Barnard, N., Hu, F. B., Jakicic, J., Lianov, L., Loveland, D., Buysse, D., Szigethy, E., Finkel, T., Sowa, G., Verschure, P., Williams, K., Sanchez, E., Dysinger, W., Maizes, V., Junker, C., Phillips, E., Katz, D., Drant, S., Jackson, R. J., Trasande, L., Woolf, S., Salive, M., South-Paul, J., States, S. L., Roth, L., Fraser, G., Stout, R., Parkinson, M. D., (2020). Prioritized research for the prevention, treatment, and reversal of chronic disease: recommendations from the lifestyle medicine research summit Frontiers in Medicine 7, 585744

Declining life expectancy and increasing all-cause mortality in the United States have been associated with unhealthy behaviors, socioecological factors, and preventable disease. A growing body of basic science, clinical research, and population health evidence points to the benefits of healthy behaviors, environments and policies to maintain health and prevent, treat, and reverse the root causes of common chronic diseases. Similarly, innovations in research methodologies, standards of evidence, emergence of unique study cohorts, and breakthroughs in data analytics and modeling create new possibilities for producing biomedical knowledge and clinical translation. To understand these advances and inform future directions research, The Lifestyle Medicine Research Summit was convened at the University of Pittsburgh on December 4–5, 2019. The Summit's goal was to review current status and define research priorities in the six core areas of lifestyle medicine: plant-predominant nutrition, physical activity, sleep, stress, addictive behaviors, and positive psychology/social connection. Forty invited subject matter experts (1) reviewed existing knowledge and gaps relating lifestyle behaviors to common chronic diseases, such as cardiovascular disease, diabetes, many cancers, inflammatory- and immune-related disorders and other conditions; and (2) discussed the potential for applying cutting-edge molecular, cellular, epigenetic and emerging science knowledge and computational methodologies, research designs, and study cohorts to accelerate clinical applications across all six domains of lifestyle medicine. Notably, federal health agencies, such as the Department of Defense and Veterans Administration have begun to adopt “whole-person health and performance” models that address these lifestyle and environmental root causes of chronic disease and associated morbidity, mortality, and cost. Recommendations strongly support leveraging emerging research methodologies, systems biology, and computational modeling in order to accelerate effective clinical and population solutions to improve health and reduce societal costs. New and alternative hierarchies of evidence are also be needed in order to assess the quality of evidence and develop evidence-based guidelines on lifestyle medicine. Children and underserved populations were identified as prioritized groups to study. The COVID-19 pandemic, which disproportionately impacts people with chronic diseases that are amenable to effective lifestyle medicine interventions, makes the Summit's findings and recommendations for future research particularly timely and relevant.

Keywords: Chronic disease, Epigenetics, In silico modeling, Inflammation, Lifestyle medicine, Nutrition, Physical activity, Research methodologies


López-Carral, Héctor, Grechuta, Klaudia, Verschure, P., (2020). Subjective ratings of emotive stimuli predict the impact of the COVID-19 quarantine on affective states PLoS ONE PLOS ONE , 15, (8), e0237631

The COVID-19 crisis resulted in a large proportion of the world’s population having to employ social distancing measures and self-quarantine. Given that limiting social interaction impacts mental health, we assessed the effects of quarantine on emotive perception as a proxy of affective states. To this end, we conducted an online experiment whereby 112 participants provided affective ratings for a set of normative images and reported on their well-being during COVID-19 self-isolation. We found that current valence ratings were significantly lower than the original ones from 2015. This negative shift correlated with key aspects of the personal situation during the confinement, including working and living status, and subjective well-being. These findings indicate that quarantine impacts mood negatively, resulting in a negatively biased perception of emotive stimuli. Moreover, our online assessment method shows its validity for large-scale population studies on the impact of COVID-19 related mitigation methods and well-being.


Puigbò, J. Y., Arsiwalla, X. D., González-Ballester, M. A., Verschure, P., (2020). Switching operation modes in the neocortex via cholinergic neuromodulation Molecular Neurobiology 57, (1), 139-149

In order to deal with the uncertainty in the world, our brains need to be able to flexibly switch between the exploration of new sensory representations and exploitation of previously acquired ones. This requires forming accurate estimations of what and how much something is expected. While modeling has allowed for the development of several ways to form predictions, how the brain could implement those is still under debate. Here, we recognize acetylcholine as one of the main neuromodulators driving learning based on uncertainty, promoting the exploration of new sensory representations. We identify its interactions with cortical inhibitory interneurons and derive a biophysically grounded computational model able to capture and learn from uncertainty. This model allows us to understand inhibition beyond gain control by suggesting that different interneuron subtypes either encode predictions or estimate their uncertainty, facilitating detection of unexpected cues. Moreover, we show how acetylcholine-like neuromodulation uniquely interacts with global and local sources of inhibition, disrupting perceptual certainty and promoting the rapid acquisition of new perceptual cues. Altogether, our model proposes that cortical acetylcholine favors sensory exploration over exploitation in a cortical microcircuit dedicated to estimating sensory uncertainty.


Amil, Adrián F., Puigbó, J.-Y., Verschure, P., (2020). Cholinergic control of chaos and evidence sensitivity in a neocortical model of perceptual decision-making Biomimetic and Biohybrid Systems 9th International Conference, Living Machines 2020 (Lecture Notes in Computer Science) , Springer International Publishing (Freiburg, Germany) 12413, 92-96

Perceptual decision-making in the brain is commonly modeled as a competition among tuned cortical populations receiving stimulation according to their perceptual evidence. However, the contribution of evidence on the decision-making process changes through time. In this regard, the mechanisms controlling the sensitivity to perceptual evidence remain unknown. Here we explore this issue by using a biologically constrained model of the neocortex performing a dual-choice perceptual discrimination task. We combine mutual and global GABAergic inhibition, which are differentially regulated by acetylcholine (ACh), a neuromodulator linked to enhanced stimulus discriminability. We find that, while mutual inhibition determines the phase-space separation between two stable attractors representing each stimulus, global inhibition controls the formation of a chaotic attractor in-between the two, effectively protecting the weakest stimulus. Hence, under low ACh levels, where global inhibition dominates, the decision-making process is chaotic and less determined by the difference between perceptual evidences. On the contrary, under high ACh levels, where mutual inhibition dominates, the network becomes very sensitive to small differences between stimuli. Our results are in line with the putative role of ACh in enhanced stimulus discriminability and suggest that ACh levels control the sensitivity to sensory inputs by regulating the amount of chaos.

Keywords: Acetylcholine, Cortical model, Decision-making, Chaos


Blancas, Maria, Valero, Cristina, Mura, Anna, Vouloutsi, Vasiliki, Verschure, P., (2020). "CREA": An inquiry-based methodology to teach robotics to children Robotics in Education International Conference on Robotics in Education (RiE) , Springer International Publishing (Vienna, Austria) Advances in Intelligent Systems and Computing (AISC, volume 1023), 45-51

Learning programming and robotics offers the opportunity to practice problem-solving, creativity, and team-work and it provides important competencies to train for the 21st century. However, programming can be challenging, and children may encounter difficulties in learning the syntax or using the coding environment. To address this issue, we have developed a methodology for teaching programming, design and robotics based on inquiry-based learning and hands-on oriented activities together with visual programming. We have applied and evaluated this new methodology within the extracurricular activity of an international elementary school in Barcelona. Our findings showed acquisition and learning of technical language, understanding of electronics devices, understanding the mapping of coding into action via the robot’s behavior. This suggests that our approach is a valid and effective teaching methodology for the instructional design of robotics and programming.

Keywords: Educational technology, Instructional design, Robotics


Guerrero, O., Verschure, P., (2020). Distributed adaptive control: An ideal cognitive architecture candidate for managing a robotic recycling plant Biomimetic and Biohybrid Systems 9th International Conference, Living Machines 2020 (Lecture Notes in Computer Science) , Springer International Publishing (Freiburg, Germany) 12413, 153-164

In the past decade, society has experienced notable growth in a variety of technological areas. However, the Fourth Industrial Revolution has not been embraced yet. Industry 4.0 imposes several challenges which include the necessity of new architectural models to tackle the uncertainty that open environments represent to cyber-physical systems (CPS). Waste Electrical and Electronic Equipment (WEEE) recycling plants stand for one of such open environments. Here, CPSs must work harmoniously in a changing environment, interacting with similar and not so similar CPSs, and adaptively collaborating with human workers. In this paper, we support the Distributed Adaptive Control (DAC) theory as a suitable Cognitive Architecture for managing a recycling plant. Specifically, a recursive implementation of DAC (between both single-agent and large-scale levels) is proposed to meet the expected demands of the European Project HR-Recycler. Additionally, with the aim of having a realistic benchmark for future implementations of the recursive DAC, a micro-recycling plant prototype is presented.

Keywords: Cognitive architecture, Distributed Adaptive Control, Recycling plant, Navigation, Motor control, Human-Robot Interaction


Freire, Ismael T., Urikh, D., Arsiwalla, X. D., Verschure, P., (2020). Machine morality: From harm-avoidance to human-robot cooperation Biomimetic and Biohybrid Systems 9th International Conference, Living Machines 2020 (Lecture Notes in Computer Science) , Springer International Publishing (Freiburg, Germany) 12413, 116-127

We present a new computational framework for modeling moral decision-making in artificial agents based on the notion of ‘Machine Morality as Cooperation’. This framework integrates recent advances from cross-disciplinary moral decision-making literature into a single architecture. We build upon previous work outlining cognitive elements that an artificial agent would need for exhibiting latent morality, and we extend it by providing a computational realization of the cognitive architecture of such an agent. Our work has implications for cognitive and social robotics. Recent studies in human neuroimaging have pointed to three different decision-making processes, Pavlovian, model-free and model-based, that are defined by distinct neural substrates in the brain. Here, we describe how computational models of these three cognitive processes can be implemented in a single cognitive architecture by using the distributed and hierarchical organization proposed by the DAC theoretical framework. Moreover, we propose that a pro-social drive to cooperate exists at the Pavlovian level that can also bias the rest of the decision system, thus extending current state-of-the-art descriptive models based on harm-aversion.

Keywords: Morality, Moral decision-making, Computational models, Cognitive architectures, Cognitive robotics, Human-robot interaction


Vouloutsi, V., Chesson, A., Blancas, M., Guerrero, O., Verschure, P., (2020). The use of social sensorimotor contingencies in humanoid robots Biomimetic and Biohybrid Systems 9th International Conference, Living Machines 2020 (Lecture Notes in Computer Science) , Springer International Publishing (Freiburg, Germany) 12413, 378-389

This pilot study investigates the role of social sensorimotor contingencies as exhibited from a humanoid robot to allow mutual understanding and social entrainment in a group social activity. The goal is to evaluate whether sensorimotor contingencies can lead to transparent and understandable interactions while we explore the dimension of personality. We propose the task of taking a selfie with a robot and a group of humans as the benchmark to evaluate the social sensorimotor contingencies displayed. We have constructed two models of interaction with an introverted and extroverted robot. We also seek to address the gap in research in context and personality of social sensorimotor contingencies in HRI and contribute to the field of personality in social robotics by determining what type of behaviour of the robot attracts certain personalities in humans in group settings. Although the sample size was small, and there were no significant differences between conditions, results suggest that the expression of sensorimotor contingencies can lead to successful coupling and interactions.

Keywords: Human-robot interaction, Personality, Social robots, Social sensorimotor contingencies


Vouloutsi, Vasiliki, Mura, Anna, Tauber, F., Speck, T., Prescott, T. J., Verschure, P., (2020). Biomimetic and Biohybrid Systems 9th International Conference, Living Machines 2020, Freiburg, Germany, July 28–30, 2020, Proceedings , Springer, Cham (Lausanne, Switzerland) 12413, 1-428

This book constitutes the proceedings of the )th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020, held in Freiburg, Germany, in July 2020. Due to COVID-19 pandemic the conference was held virtually. The 32 full and 7 short papers presented in this volume were carefully reviewed and selected from 45 submissions. They deal with research on novel life-like technologies inspired by the scientific investigation of biological systems, biomimetics, and research that seeks to interface biological and artificial systems to create biohybrid systems.

Keywords: Artificial intelligence, Soft robotics, Biomimetics, Insect navigation, Synthetic nervous system, Computer vision, Bio-inspired materials, Visual homing, Locomotion+, Image processing, Intelligent robots, Human-robot interaction, Machine learning, Snake robot, Mobile robots, Robotic systems, Drosophila, Robots, Sensors, Signal processing


Chernigovskaya, T. V., Parina, I. S., Alekseeva, S. V., Konina, A. A., Urich, D. K., Mansurova, Y. O., Parin, S. B., (2019). Simultaneous interpreting: Characteristic of autonomic provision of extreme cognitive loads Sovremennye Tehnologii v Medicine 11, (1), 132-138

Simultaneous interpreting is one of the most comprehensive and energy-consuming types of cognitive activity. To work successfully, a simultaneous interpreter must have a specific functional state. The aim of our study was to find out the basic mechanisms of this functional state, the effect of the simultaneous interpreting on cognitive function changes, and the main factors influencing the degree of the regulatory systems strain. Materials and Methods. 33 individuals participated in the study: 22 linguists specially trained in simultaneous translation composed the experimental group and 11 language-qualified people having no skills of simultaneous translation represented the control group. In compliance with the study design, the measurements were performed under the conditions similar to the real work of simultaneous interpreters: The participants working in succession performed professional tasks: Shadowing in the native and foreign languages (German and English), simultaneous interpretation of the reports from the native language to the foreign, and vice versa. The interpreters were psychologically tested using ApWay.ru Web platform before and after the performance on the professional tasks: Computer campimetry, test for a simple sensorimotor activity, Stroop test, and test for emotional disadaptation level. Cardiointervalogram was telemetrically recorded during the entire experiment. Results. Some specific aspects of autonomic provision of simultaneous interpreting have been unraveled. A significantly greater tension of the autonomic regulation is manifested by the simultaneous interpreters compared to the control group. It was most prominent when translation was done from the foreign language. The total level of stress during the performance on the linguistic tasks appeared to be higher in the control group. In the simultaneous interpreters, in contrast to the control group, there was registered a high activity level of the sympathetic and parasympathetic systems and a marked integration of the cardiac rhythm regulation circuits over the entire period of performing the linguistic tasks. The psychological tests have demonstrated a significantly more confident cognitive control relative to the control group. Thus, a specific functional system has been formed in the simultaneous interpreters providing a successful interaction of various information images (or codes) and consolidation of autonomic and cognitive resources during the performance on professional tasks. Lack of the necessary skills and, consequently, of the task-oriented functional system in the participants of the control group resulted in the enhancement of the non-specific (less effective) stress response.

Keywords: Autonomic provision of simultaneous interpreting, Cognitive activity, Cognitive control, Simultaneous interpreting


Arsiwalla, X. D., Bote, R. M., Verschure, P., (2019). Beyond neural coding? Lessons from perceptual control theory Behavioral and brain sciences 42, e217

Pointing to similarities between challenges encountered in today's neural coding and twentieth-century behaviorism, we draw attention to lessons learned from resolving the latter. In particular, Perceptual Control Theory posits behavior as a closed-loop control process with immediate and teleological causes. With two examples, we illustrate how these ideas may also address challenges facing current neural coding paradigms.


Pacheco Estefan, D., Sánchez-Fibla, M., Duff, A., Principe, A., Rocamora, R., Zhang, H., Axmacher, N., Verschure, P., (2019). Coordinated representational reinstatement in the human hippocampus and lateral temporal cortex during episodic memory retrieval Nature Communications 10, (1), 2255

Theoretical models of episodic memory have proposed that retrieval depends on interactions between the hippocampus and neocortex, where hippocampal reinstatement of item-context associations drives neocortical reinstatement of item information. Here, we simultaneously recorded intracranial EEG from hippocampus and lateral temporal cortex (LTC) of epilepsy patients who performed a virtual reality spatial navigation task. We extracted stimulus-specific representations of both item and item-context associations from the time-frequency patterns of activity in hippocampus and LTC. Our results revealed a double dissociation of representational reinstatement across time and space: an early reinstatement of item-context associations in hippocampus preceded a later reinstatement of item information in LTC. Importantly, reinstatement levels in hippocampus and LTC were correlated across trials, and the quality of LTC reinstatement was predicted by the magnitude of phase synchronization between hippocampus and LTC. These findings confirm that episodic memory retrieval in humans relies on coordinated representational interactions within a hippocampal-neocortical network.


Bos, J. J., Vinck, M., Marchesi, P., Keestra, A., van Mourik-Donga, L. A., Jackson, J. C., Verschure, P., Pennartz, C. M. A., (2019). Multiplexing of self and other information in hippocampal ensembles Cell Reports 29, (12), 3859-3871.e6

In addition to coding a subject’s location in space, the hippocampus has been suggested to code social information, including the spatial position of conspecifics. “Social place cells” have been reported for tasks in which an observer mimics the behavior of a demonstrator. We examine whether rat hippocampal neurons may encode the behavior of a minirobot, but without requiring the animal to mimic it. Rather than finding social place cells, we observe that robot behavioral patterns modulate place fields coding animal position. This modulation may be confounded by correlations between robot movement and changes in the animal’s position. Although rat position indeed significantly predicts robot behavior, we find that hippocampal ensembles code additional information about robot movement patterns. Fast-spiking interneurons are particularly informative about robot position and global behavior. In conclusion, when the animal’s own behavior is conditional on external agents, the hippocampus multiplexes information about self and others.

Keywords: CA1, Decoding, Information theory, Interneuron, Mutual information, Place cells, Place field, Tobot, Docial behavior, Tetrode


Grechuta, K., Rubio Ballester, B., Espín Munne, R., Usabiaga Bernal, T., Molina Hervás, B., Mohr, B., Pulvermüller, F., San Segundo, R., Verschure, P., (2019). Augmented dyadic therapy boosts recovery of language function in patients with nonfluent aphasia Stroke 50, (5), 1270-1274

Background and Purpose- Evidence suggests that therapy can be effective in recovering from aphasia, provided that it consists of socially embedded, intensive training of behaviorally relevant tasks. However, the resources of healthcare systems are often too limited to provide such treatment at sufficient dosage. Hence, there is a need for evidence-based, cost-effective rehabilitation methods. Here, we asked whether virtual reality-based treatment grounded in the principles of use-dependent learning, behavioral relevance, and intensity positively impacts recovery from nonfluent aphasia. Methods- Seventeen patients with chronic nonfluent aphasia underwent intensive therapy in a randomized, controlled, parallel-group trial. Participants were assigned to the control group (N=8) receiving standard treatment or to the experimental group (N=9) receiving augmented embodied therapy with the Rehabilitation Gaming System for aphasia. All Rehabilitation Gaming System for aphasia sessions were supervised by an assistant who monitored the patients but did not offer any elements of standard therapy. Both interventions were matched for intensity and materials. Results- Our results revealed that at the end of the treatment both groups significantly improved on the primary outcome measure (Boston Diagnostic Aphasia Examination: control group, P=0.04; experimental group, P=0.01), and the secondary outcome measure (lexical access-vocabulary test: control group, P=0.01; experimental group, P=0.007). However, only the Rehabilitation Gaming System for aphasia group improved on the Communicative Aphasia Log ( P=0.01). The follow-up assessment (week 16) demonstrated that while both groups retained vocabulary-related changes (control group, P=0.01; experimental group, P=0.007), only the Rehabilitation Gaming System for aphasia group showed therapy-induced improvements in language ( P=0.01) and communication ( P=0.05). Conclusions- Our results demonstrate the effectiveness of Rehabilitation Gaming System for aphasia for improving language and communication in patients with chronic aphasia suggesting that current challenges faced by the healthcare system in the treatment of stroke might be effectively addressed by augmenting traditional therapy with computer-based methods. Clinical Trial Registration- URL: https://www.clinicaltrials.gov . Unique identifier: NCT02928822.

Keywords: Aphasia, Embodied training, Neurological rehabilitation, Virtual reality


Santos-Pata, Diogo, Zucca, Riccardo, López-Carral, Héctor, Verschure, P., (2019). Modulating grid cell scale and intrinsic frequencies via slow high-threshold conductances: A simplified model Neural Networks 119, 66-73

Grid cells in the medial entorhinal cortex (MEC) have known spatial periodic firing fields which provide a metric for the representation of self-location and path planning. The hexagonal tessellation pattern of grid cells scales up progressively along the MEC’s layer II dorsal-to-ventral axis. This scaling gradient has been hypothesized to originate either from inter-population synaptic dynamics as postulated by attractor networks, or from projected theta frequency waves to different axis levels, as in oscillatory models. Alternatively, cellular dynamics and specifically slow high-threshold conductances have been proposed to have an impact on the grid cell scale. To test the hypothesis that intrinsic hyperpolarization-activated cation currents account for both the scaled gradient and the oscillatory frequencies observed along the dorsal-to-ventral axis, we have modeled and analyzed data from a population of grid cells simulated with spiking neurons interacting through low-dimensional attractor dynamics. We observed that the intrinsic neuronal membrane properties of simulated cells were sufficient to induce an increase in grid scale and potentiate differences in the membrane potential oscillatory frequency. Overall, our results suggest that the after-spike dynamics of cation currents may play a major role in determining the grid cells’ scale and that oscillatory frequencies are a consequence of intrinsic cellular properties that are specific to different levels of the dorsal-to-ventral axis in the MEC layer II.

Keywords: Grid cells, Entorhinal, Hyperpolarization, Navigation, Space


Blithikioti, C., Miquel, L., Batalla, A., Rubio, B., Maffei, G., Herreros, I., Gual, A., Verschure, P., Balcells-Oliveró, M., (2019). Cerebellar alterations in cannabis users: A systematic review Addiction Biology 24, (6), 1121-1137

Cannabis is the most used illicit substance in the world. As many countries are moving towards decriminalization, it is crucial to determine whether and how cannabis use affects human brain and behavior. The role of the cerebellum in cognition, emotion, learning, and addiction is increasingly recognized. Because of its high density in CB1 receptors, it is expected to be highly affected by cannabis use. The aim of this systematic review is to investigate how cannabis use affects cerebellar structure and function, as well as cerebellar-dependent behavioral tasks. Three databases were searched for peer-reviewed literature published until March 2018. We included studies that focused on cannabis effects on cerebellar structure, function, or cerebellar-dependent behavioral tasks. A total of 348 unique records were screened, and 40 studies were included in the qualitative synthesis. The most consistent findings include (1) increases in cerebellar gray matter volume after chronic cannabis use, (2) alteration of cerebellar resting state activity after acute or chronic use, and (3) deficits in memory, decision making, and associative learning. Age of onset and higher exposure to cannabis use were frequently associated with increased cannabis-induced alterations. Chronic cannabis use is associated with alterations in cerebellar structure and function, as well as with deficits in behavioral paradigms that involve the cerebellum (eg, eyeblink conditioning, memory, and decision making). Future studies should consider tobacco as confounding factor and use standardized methods for assessing cannabis use. Paradigms exploring the functional activity of the cerebellum may prove useful as monitoring tools of cannabis-induced impairment.

Keywords: Behavior, Cannabis use, Cerebellum, Cognitive function, Structure


Maier, Martina, Rubio Ballester, Belén, Duff, Armin, Duarte Oller, Esther, Verschure, P., (2019). Effect of specific over nonspecific VR-based rehabilitation on poststroke motor recovery: A systematic meta-analysis Neurorehabilitation and Neural Repair 33, (2), 112-129

Background. Despite the rise of virtual reality (VR)-based interventions in stroke rehabilitation over the past decade, no consensus has been reached on its efficacy. This ostensibly puzzling outcome might not be that surprising given that VR is intrinsically neutral to its use—that is, an intervention is effective because of its ability to mobilize recovery mechanisms, not its technology. As VR systems specifically built for rehabilitation might capitalize better on the advantages of technology to implement neuroscientifically grounded protocols, they might be more effective than those designed for recreational gaming. Objective. We evaluate the efficacy of specific VR (SVR) and nonspecific VR (NSVR) systems for rehabilitating upper-limb function and activity after stroke. Methods. We conducted a systematic search for randomized controlled trials with adult stroke patients to analyze the effect of SVR or NSVR systems versus conventional therapy (CT). Results. We identified 30 studies including 1473 patients. SVR showed a significant impact on body function (standardized mean difference [SMD] = 0.23; 95% CI = 0.10 to 0.36; P = .0007) versus CT, whereas NSVR did not (SMD = 0.16; 95% CI = −0.14 to 0.47; P = .30). This result was replicated in activity measures. Conclusions. Our results suggest that SVR systems are more beneficial than CT for upper-limb recovery, whereas NSVR systems are not. Additionally, we identified 6 principles of neurorehabilitation that are shared across SVR systems and are possibly responsible for their positive effect. These findings may disambiguate the contradictory results found in the current literature.

Keywords: Stroke, Paresis, Virtual reality, Rehabilitation, Occupational therapy, Review


Herreros, Ivan, Miquel, Laia, Blithikioti, Chrysanthi, Nuño, Laura, Rubio Ballester, Belen, Grechuta, Klaudia, Gual, Antoni, Balcells-Oliveró, Mercè, Verschure, P., (2019). Motor adaptation impairment in chronic cannabis users assessed by a visuomotor rotation task Journal of Clinical Medicine 8, (7), 1049

Background—The cerebellum has been recently suggested as an important player in the addiction brain circuit. Cannabis is one of the most used drugs worldwide, and its long-term effects on the central nervous system are not fully understood. No valid clinical evaluations of cannabis impact on the brain are available today. The cerebellum is expected to be one of the brain structures that are highly affected by prolonged exposure to cannabis, due to its high density in endocannabinoid receptors. We aim to use a motor adaptation paradigm to indirectly assess cerebellar function in chronic cannabis users (CCUs). Methods—We used a visuomotor rotation (VMR) task that probes a putatively-cerebellar implicit motor adaptation process together with the learning and execution of an explicit aiming rule. We conducted a case-control study, recruiting 18 CCUs and 18 age-matched healthy controls. Our main measure was the angular aiming error. Results—Our results show that CCUs have impaired implicit motor adaptation, as they showed a smaller rate of adaptation compared with healthy controls (drift rate: 19.3 +/− 6.8° vs. 27.4 +/− 11.6°; t(26) = −2.1, p = 0.048, Cohen’s d = −0.8, 95% CI = (−1.7, −0.15)). Conclusions—We suggest that a visuomotor rotation task might be the first step towards developing a useful tool for the detection of alterations in implicit learning among cannabis users.

Keywords: Cerebellum, Cannabis, Implicit motor learning, Motor adaptation, Visuomotor rotation


Maier, Martina, Ballester, Belén Rubio, Verschure, P., (2019). Principles of neurorehabilitation after stroke based on motor learning and brain plasticity mechanisms Frontiers in Systems Neuroscience 13, 74

What are the principles underlying effective neurorehabilitation? The aim of neurorehabilitation is to exploit interventions based on human and animal studies about learning and adaptation, as well as to show that the activation of experience-dependent neuronal plasticity augments functional recovery after stroke. Instead of teaching compensatory strategies that do not reduce impairment but allow the patient to return home as soon as possible, functional recovery might be more sustainable as it ensures a long-term reduction in impairment and an improvement in quality of life. At the same time, neurorehabilitation permits the scientific community to collect valuable data, which allows inferring about the principles of brain organization. Hence neuroscience sheds light on the mechanisms of learning new functions or relearning lost ones. However, current rehabilitation methods lack the exact operationalization of evidence gained from skill learning literature, leading to an urgent need to bridge motor learning theory and present clinical work in order to identify a set of ingredients and practical applications that could guide future interventions. This work aims to unify the neuroscientific literature relevant to the recovery process and rehabilitation practice in order to provide a synthesis of the principles that constitute an effective neurorehabilitation approach. Previous attempts to achieve this goal either focused on a subset of principles or did not link clinical application to the principles of motor learning and recovery. We identified 15 principles of motor learning based on existing literature: massed practice, spaced practice, dosage, task-specific practice, goal-oriented practice, variable practice, increasing difficulty, multisensory stimulation, rhythmic cueing, explicit feedback/knowledge of results, implicit feedback/knowledge of performance, modulate effector selection, action observation/embodied practice, motor imagery, and social interaction. We comment on trials that successfully implemented these principles and report evidence from experiments with healthy individuals as well as clinical work.

Keywords: Neurorehabilitation, Motor learning, Plasticity, Stroke, Principles


Grechuta, Klaudia, Ulysse, Laura, Rubio Ballester, Belén, Verschure, Paul, (2019). Self beyond the body: Action-driven and task-relevant purely distal cues modulate performance and body ownership Frontiers in Human Neuroscience 13, Article 91

Our understanding of body ownership largely relies on the so-called Rubber Hand Illusion (RHI). In this paradigm, synchronous stroking of the real and the rubber hands leads to an illusion of ownership of the rubber hand provided that it is physically, anatomically, and spatially plausible. Self-attribution of an artificial hand also occurs during visuomotor synchrony. In particular, participants experience ownership over a virtual or a rubber hand when the visual feedback of self-initiated movements follows the trajectory of the instantiated motor commands, such as in the Virtual Hand Illusion (VHI) or the moving Rubber Hand Illusion (mRHI). Evidence yields that both when the cues are triggered externally (RHI) and when they result from voluntary actions (VHI and mRHI), the experience of ownership is established through bottom-up integration and top-down prediction of proximodistal cues (visuotactile or visuomotor) within the peripersonal space. It seems, however, that depending on whether the sensory signals are externally (RHI) or self-generated (VHI and mRHI), the top-down expectation signals are qualitatively different. On the one hand, in the RHI the sensory correlations are modulated by top-down influences which constitute empirically induced priors related to the internal (generative) model of the body. On the other hand, in the VHI and mRHI body ownership is actively shaped by processes which allow for continuous comparison between the expected and the actual sensory consequences of the actions. Ample research demonstrates that the differential processing of the predicted and the reafferent information is addressed by the central nervous system via an internal (forward) model or corollary discharge. Indeed, results from the VHI and mRHI suggest that, in action-contexts, the mechanism underlying body ownership could be similar to the forward model. Crucially, forward models integrate across all self-generated sensory signals including not only proximodistal (i.e., visuotactile or visuomotor) but also purely distal sensory cues (i.e., visuoauditory). Thus, if body ownership results from a consistency of a forward model, it will be affected by the (in)congruency of purely distal cues provided that they inform about action-consequences and are relevant to a goal-oriented task. Specifically, they constitute a corrective error signal. Here, we explicitly addressed this question. To test our hypothesis, we devised an embodied virtual reality-based motor task where action outcomes were signaled by distinct auditory cues. By manipulating the cues with respect to their spatial, temporal and semantic congruency, we show that purely distal (visuoauditory) feedback which violates predictions about action outcomes compromises both performance and body ownership. These results demonstrate, for the first time, that body ownership is influenced by not only externally and self-generated cues which pertain to the body within the peripersonal space but also those arising outside of the body. Hence, during goal-oriented tasks body ownership may result from the consistency of forward models.

Keywords: Body ownership, Internal forward model, Goal-oriented behavior, Multisensory integration, Top-down prediction


Ballester, B. R., Maier, M., Duff, A., Cameirão, M., Bermúdez, S., Duarte, E., Cuxart, A., Rodríguez, S., San Segundo Mozo, R. M., Verschure, P., (2019). A critical time window for recovery extends beyond one-year post-stroke Journal of neurophysiology Journal of Neurophysiology , 122, (1), 350-357

The impact of rehabilitation on post-stroke motor recovery and its dependency on the patient's chronicity remain unclear. The field has widely accepted the notion of a proportional recovery rule with a "critical window for recovery" within the first 3-6 mo poststroke. This hypothesis justifies the general cessation of physical therapy at chronic stages. However, the limits of this critical window have, so far, been poorly defined. In this analysis, we address this question, and we further explore the temporal structure of motor recovery using individual patient data from a homogeneous sample of 219 individuals with mild to moderate upper-limb hemiparesis. We observed that improvement in body function and structure was possible even at late chronic stages. A bootstrapping analysis revealed a gradient of enhanced sensitivity to treatment that extended beyond 12 mo poststroke. Clinical guidelines for rehabilitation should be revised in the context of this temporal structure. NEW & NOTEWORTHY Previous studies in humans suggest that there is a 3- to 6-mo "critical window" of heightened neuroplasticity poststroke. We analyze the temporal structure of recovery in patients with hemiparesis and uncover a precise gradient of enhanced sensitivity to treatment that expands far beyond the limits of the so-called critical window. These findings highlight the need for providing therapy to patients at the chronic and late chronic stages.

Keywords: Motor recovery, Neuroplasticity, Neurorehabilitation, Stroke recovery, Virtual reality


Bortolla, Roberta, Cavicchioli, Marco, Galli, Marco, Verschure, P., Maffei, Cesare, (2019). A comprehensive evaluation of emotional responsiveness in borderline personality disorder: a support for hypersensitivity hypothesis Borderline Personality Disorder and Emotion Dysregulation 6, (1), 8

Background: Many experimental studies have evaluated Linehan’s biological emotional vulnerability in Borderline Personality Disorder (BPD). However, some inconsistencies were observed in operationalizing and supporting its components. This study aims at clarifying which aspects of Linehan’s model are altered in BPD, considering a multimodal evaluation of processes concerned with emotional responsiveness (self-report, psychophysiology and eye-tracking). Methods: Forty-eight socio-emotional pictures were administered to 28 participants (14 BPD, 14 Healthy Controls, HCs), gender- and age-matched, by employing two different lengths of stimuli exposure (5 s and 15 s). Results: Our results supported the hypersensitivity hypothesis in terms of faster physiological responses and altered visual processing. Furthermore, hypersensitivity was associated with detailed socio-emotional contents. Hyperreactivity assumption was not experimentally sustained by physiological and self-report data. Ultimately, the slow return to emotional baseline was demonstrated as an impaired emotional modulation. Conclusions: Our data alternatively supported the hypersensitivity and the slow return to emotional baseline hypotheses, postulated by Linehan’s Biosocial model, rather than the hyperreactivity assumption. Results have been discussed in light of other BPD core psychopathological processes.

Keywords: Borderline personality disorder, Emotional vulnerability, Linehan’s model, Hypersensitivity, Slow return to emotional baseline


Rubio Ballester, B., Mura, A., Maier, M., Tobella-Pareja, Laura, Alfayate-Domingo, D., Gimeno-Esteve, M. F., Aguilar, A., Verschure, P., (2019). Adaptive VR-based rehabilitation to prevent deterioration in adults with cerebral palsy Application of VR and Advanced Technology in Pediatric Populations International Conference on Virtual Rehabilitation 2019 (ICVR 2019) , ISVR (Tel Aviv, Israel) , 1-7

Cerebral palsy (CP) is a disabling life-long condition progressively impeding a patient’s independence. Although incident rates are high, a clear understanding of the disease is missing. CP is characterized by several motor disorders and sensory or perceptive comorbidities. This multifaceted nature complicates proper diagnosis and hampers the search for possible treatments. During adolescence and adulthood, individuals with CP experience a drastic deterioration in gross motor control, independence, and quality of life. There is poor evidence that physical therapy promotes the retention of function through aging, and no clinical studies exist that explore the potential of VRbased training to prevent deterioration. In this pilot randomized controlled trial, we expose 14 adults with CP to the Rehabilitation Gaming System (RGS) and examine its usability, effectiveness, and acceptability. Our results show that the RGS difficulty adaptation algorithm automatically matches the patients' impairment level as captured by clinical scales (Barthel and Box & Blocks). The clinical effectiveness and acceptability of the RGS and conventional therapy were comparable. We conclude that VR-based physical therapy as an adjunct to usual treatment may be a promising approach for the prevention of deterioration in adolescents and adults with CP.

Keywords: Cerebral palsy, Virtual realitY, Motor function, Physical therapy, Rehabilitation


Feitosa, J. A., Stefano Filho, C. A., Casseb, R. F., Camargo, A., Martins, B. S. G., Ballester, B. R., Omedas, P., Verschure, P., Oberg, T. D., Min, L. L., Castellano, G., (2019). Complex network changes during a virtual reality rehabilitation protocol following stroke: A case study NER 2019 9th International IEEE/EMBS Conference on Neural Engineering , IEEE (San Francisco, USA) , 891-894

Stroke is one of the main causes of disabilities caused by injuries to the human central nervous system, yielding a wide range of mild to severe impairments that can compromise sensorimotor and cognitive functions. Although rehabilitation protocols may improve function of stroke survivors, patients often reach plateaus while undergoing therapy. Recently, virtual reality (VR) technologies have been paired with traditional rehabilitation aiming to improve function recovery after stroke. Aiming to better understand structural brain changes due to VR rehabilitation protocols, we modeled the brain as a graph and extracted three measures representing the network's topology: degree, clustering coefficient and betweenness centrality (BC). In this single case study, our results indicate that all metrics increased on the ipsilesional hemisphere, while remaining about the same at the contrale-sional site. Particularly, the number of functional connections increased in the lesion area overtime. In addition, the BC displayed the highest variations, and in brain regions related to the patient's cognitive and motor impairments; hence, we argue that this measure could be regarded as an indicative for brain plasticity mechanisms.


Maier, M., Low, S. C., Ballester, B. R., Bañuelos, N. L., Oller, E. D., Verschure, P., (2019). Depression modulates attentional processing after stroke Biosystems & Biorobotics International Conference on NeuroRehabilitation - Converging Clinical and Engineering Research on Neurorehabilitation III , Springer, Cham (Pisa, Italy) 21, 702-706

Depression is a common sequela after stroke and has severe implications on a patient’s life. Post-stroke depression has been linked to cognitive impairment, but the mechanisms that lead to this deficit are not well understood. We tested 18 chronic stroke patients with depression in a psychophysical task to evaluate their attentional processing under varying cognitive loads. We found that the level of depression had no effect on the unconscious, bottom-up components of attentional processing but did influence the top-down ones. These results support the notion that depression might act like an additional cognitive load, impeding the conscious processes and responses although the information has been unconsciously processed.


Vouloutsi, V., Grechuta, K., Verschure, P., (2019). Evaluation of the facial expressions of a humanoid robot Biomimetic and Biohybrid Systems 8th International Conference, Living Machines 2019 (Lecture Notes in Computer Science) , Springer International Publishing (Nara, Japan) 11556, 365-368

Facial expressions are salient social features that crucial in communication, and humans are capable of reading the messages faces convey and the emotions they display. Robots that interact with humans will need to employ similar communication channels for successful interactions. Here, we focus on the readability of the facial expressions of a humanoid robot. We conducted an online survey where participants evaluated emotional stimuli and assessed the robot’s expressions. Results suggest that the robot’s facial expressions are correctly recognised and the appraisal of the robots expressive elements are consistent with the literature.

Keywords: Emotion recognition, Facial expressions, Human-robot interaction


López-Carral, Héctor, Santos-Pata, D., Zucca, R., Verschure, P., (2019). How you type is what you type: Keystroke dynamics correlate with affective content ACII 2019 8th International Conference on Affective Computing and Intelligent Interaction , IEEE (Cabride, UK) , 1-5

Estimating the affective state of a user during a computer task traditionally relies on either subjective reports or analysis of physiological signals, facial expressions, and other measures. These methods have known limitations, can be intrusive and may require specialized equipment. An alternative would be employing a ubiquitous device of everyday use such as a standard keyboard. Here we investigate if we can infer the emotional state of a user by analyzing their typing patterns. To test this hypothesis, we asked 400 participants to caption a set of emotionally charged images taken from a standard database with known ratings of arousal and valence. We computed different keystroke pattern dynamics, including keystroke duration (dwell time) and latency (flight time). By computing the mean value of all of these features for each image, we found a statistically significant negative correlation between dwell times and valence, and between flight times and arousal. These results highlight the potential of using keystroke dynamics to estimate the affective state of a user in a non-obtrusive way and without the need for specialized devices.

Keywords: Feature extraction, Correlation, Keyboards, Task analysis, Statistical analysis, Affective computing, Standards, Keystroke, Keyboard, Typing, Arousal, Valence, Affect


Arsiwalla, X. D., Freire, I. T., Vouloutsi, V., Verschure, P., (2019). Latent morality in algorithms and machines Biomimetic and Biohybrid Systems 8th International Conference, Living Machines 2019 (Lecture Notes in Computer Science) , Springer, Cham (Nara, Japan) 11556, 309-315

Can machines be endowed with morality? We argue that morality in the descriptive or epistemic sense can be extended to artificial systems. Following arguments from evolutionary game-theory, we identify two main ingredients required to operationalize this notion of morality in machines. The first, being a group theory of mind, and the second, being an assignment of valence. We make the case for the plausibility of these operations in machines without reference to any form of intentionality or consciousness. The only systems requirements needed to support the above two operations are autonomous goal-directed action and the ability to interact and learn from the environment. Following this we have outlined a theoretical framework based on conceptual spaces and valence assignments to gauge latent morality in autonomous machines and algorithms.

Keywords: Autonomous systems, Ethics of algorithms, Goal-directed action, Philosophy of morality, Qualia, Theory of mind


Amil, A. F., Maffei, G., Puigbò, J. Y., Arsiwalla, X. D., Verschure, P., (2019). Robust postural stabilization with a biomimetic hierarchical control architecture Biomimetic and Biohybrid Systems 8th International Conference, Living Machines 2019 (Lecture Notes in Computer Science) , Springer, Cham (Nara, Japan) 11556, 321-324

Fast online corrections during anticipatory movements are a signature of robustness in biological motor control. In this regard, a previous study suggested that anticipatory postural control can be recast as a sensory-sensory predictive process, where hierarchically connected cerebellar microcircuits reflect the causal sequence of events preceding a postural disturbance. Hence, error monitoring signals from higher sensory layers inform lower layers about violations of expectations, affording fast corrections when the normal sequence is broken. Here we generalize this insight and prove that the proposed hierarchical control architecture can deal with different types of alterations in the causal structure of the environment, therefore extending the limits of performance.

Keywords: Anticipatory control, Cerebellum, Control architecture, Robustness


Martinez-Hernandez, Uriel, Vouloutsi, Vasiliki, Mura, Anna, Mangan, Michael, Asada, Minoru, Prescott, T. J., Verschure, P., (2019). Biomimetic and Biohybrid Systems 8th International Conference, Living Machines 2019, Nara, Japan, July 9–12, 2019, Proceedings , Springer, Cham (Lausanne, Switzerland) 11556, 1-384

This book constitutes the proceedings of the 8th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2019, held in Nara, Japan, in July 2019. The 26 full and 16 short papers presented in this volume were carefully reviewed and selected from 45 submissions. They deal with research on novel life-like technologies inspired by the scientific investigation of biological systems, biomimetics, and research that seeks to interface biological and artificial systems to create biohybrid systems.

Keywords: Artificial intelligence, Biomimetics, Computer architecture, Human robot interaction, Human-Computer Interaction (HCI), Humanoid robot, Image processing, Learning algorithms, Mobile robots, Multipurpose robots, Neural networks, Quadruped robots, Reinforcement learning, Robot learning, Robotics, Robots, Sensor, Sensors, Swarm robotics, User interfaces


Fischer, Tobias, Puigbò, Jordi-Ysard, Camilleri, Daniel, Nguyen, Phuong D. H., Moulin-Frier, Clément, Lallée, Stéphane, Metta, Giorgio, Prescott, Tony J., Demiris, Yiannis, Verschure, P., (2018). iCub-HRI: A software framework for complex human-robot interaction scenarios on the iCub humanoid robot Frontiers in Robotics and AI , 5, (22), Article 22

Generating complex, human-like behaviour in a humanoid robot like the iCub requires the integration of a wide range of open source components and a scalable cognitive architecture. Hence, we present the iCub-HRI library which provides convenience wrappers for components related to perception (object recognition, agent tracking, speech recognition, touch detection), object manipulation (basic and complex motor actions) and social interaction (speech synthesis, joint attention) exposed as a C++ library with bindings for Java (allowing to use iCub-HRI within Matlab) and Python. In addition to previously integrated components, the library allows for simple extension to new components and rapid prototyping by adapting to changes in interfaces between components. We also provide a set of modules which make use of the library, such as a high-level knowledge acquisition module and an action recognition module. The proposed architecture has been successfully employed for a complex human-robot interaction scenario involving the acquisition of language capabilities, execution of goal-oriented behaviour and expression of a verbal narrative of the robot's experience in the world. Accompanying this paper is a tutorial which allows a subset of this interaction to be reproduced. The architecture is aimed at researchers familiarising themselves with the iCub ecosystem, as well as expert users, and we expect the library to be widely used in the iCub community.

Keywords: Robotics, iCub Humanoid, YARP, Software architecture, C++, Python, Java, Human-robot interaction


Puigbò, J. Y., Maffei, G., Herreros, I., Ceresa, M., González Ballester, M. A., Verschure, P. F. M. J., (2018). Cholinergic behavior state-dependent mechanisms of neocortical gain control: A neurocomputational study Molecular Neurobiology 55, (1), 249-257

The embodied mammalian brain evolved to adapt to an only partially known and knowable world. The adaptive labeling of the world is critically dependent on the neocortex which in turn is modulated by a range of subcortical systems such as the thalamus, ventral striatum, and the amygdala. A particular case in point is the learning paradigm of classical conditioning where acquired representations of states of the world such as sounds and visual features are associated with predefined discrete behavioral responses such as eye blinks and freezing. Learning progresses in a very specific order, where the animal first identifies the features of the task that are predictive of a motivational state and then forms the association of the current sensory state with a particular action and shapes this action to the specific contingency. This adaptive feature selection has both attentional and memory components, i.e., a behaviorally relevant state must be detected while its representation must be stabilized to allow its interfacing to output systems. Here, we present a computational model of the neocortical systems that underlie this feature detection process and its state-dependent modulation mediated by the amygdala and its downstream target the nucleus basalis of Meynert. In particular, we analyze the role of different populations of inhibitory interneurons in the regulation of cortical activity and their state-dependent gating of sensory signals. In our model, we show that the neuromodulator acetylcholine (ACh), which is in turn under control of the amygdala, plays a distinct role in the dynamics of each population and their associated gating function serving the detection of novel sensory features not captured in the state of the network, facilitating the adjustment of cortical sensory representations and regulating the switching between modes of attention and learning.

Keywords: Acetylcholine, Inhibitory network, Neocortical circuits, Neuromodulation


Zamora, R., Korff, S., Mi, Q., Barclay, D., Schimunek, L., Zucca, R., Arsiwalla, X. D., Simmons, R. L., Verschure, P., Billiar, T. R., Vodovotz, Y., (2018). A computational analysis of dynamic, multi-organ inflammatory crosstalk induced by endotoxin in mice PLoS Computational Biology 14, (11), e1006582

Bacterial lipopolysaccharide (LPS) induces an acute inflammatory response across multiple organs, primarily via Toll-like receptor 4 (TLR4). We sought to define novel aspects of the complex spatiotemporal dynamics of LPS-induced inflammation using computational modeling, with a special focus on the timing of pathological systemic spillover. An analysis of principal drivers of LPS-induced inflammation in the heart, gut, lung, liver, spleen, and kidney to assess organ-specific dynamics, as well as in the plasma (as an assessment of systemic spillover), was carried out using data on 20 protein-level inflammatory mediators measured over 0-48h in both C57BL/6 and TLR4-null mice. Using a suite of computational techniques, including a time-interval variant of Principal Component Analysis, we confirm key roles for cytokines such as tumor necrosis factor-α and interleukin-17A, define a temporal hierarchy of organ-localized inflammation, and infer the point at which organ-localized inflammation spills over systemically. Thus, by employing a systems biology approach, we obtain a novel perspective on the time- and organ-specific components in the propagation of acute systemic inflammation.


Arsiwalla, Xerxes D., Verschure, Paul, (2018). Measuring the complexity of consciousness Frontiers in Neuroscience 12, (424), Article 424

The grand quest for a scientific understanding of consciousness has given rise to many new theoretical and empirical paradigms for investigating the phenomenology of consciousness as well as clinical disorders associated to it. A major challenge in this field is to formalize computational measures that can reliably quantify global brain states from data. In particular, information-theoretic complexity measures such as integrated information have been proposed as measures of conscious awareness. This suggests a new framework to quantitatively classify states of consciousness. However, it has proven increasingly difficult to apply these complexity measures to realistic brain networks. In part, this is due to high computational costs incurred when implementing these measures on realistically large network dimensions. Nonetheless, complexity measures for quantifying states of consciousness are important for assisting clinical diagnosis and therapy. This article is meant to serve as a lookup table of measures of consciousness, with particular emphasis on clinical applicability. We consider both, principle-based complexity measures as well as empirical measures tested on patients. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions. We address challenges facing these measures with regard to realistic brain networks, and where necessary, suggest possible resolutions.

Keywords: Consciousness in the Clinic, Computational neuroscience, Complexity measures, Clinical Neuroscience, Measures of consciousness


Moulin-Frier, C., Fischer, T., Petit, M., Pointeau, G., Puigbo, J., Pattacini, U., Low, S. C., Camilleri, D., Nguyen, P., Hoffmann, M., Chang, H. J., Zambelli, M., Mealier, A., Damianou, A., Metta, G., Prescott, T. J., Demiris, Y., Dominey, P. F., Verschure, P. F. M. J., (2018). DAC-h3: A proactive robot cognitive architecture to acquire and express knowledge about the world and the self IEEE Transactions on Cognitive and Developmental Systems 10, (4), 1005-1022

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.

Keywords: Autobiographical Memory., Biology, Cognition, Cognitive Robotics, Computer architecture, Distributed Adaptive Control, Grounding, Human-Robot Interaction, Humanoid robots, Robot sensing systems, Symbol Grounding


Santos-Pata, D., Verschure, P., (2018). Human vicarious trial and error is predictive of spatial navigation performance Frontiers in Behavioral Neuroscience 12, Article 237

When learning new environments, rats often pause at decision points and look back and forth over their possible trajectories as if they were imagining the future outcome of their actions, a behavior termed “Vicarious trial and error” (VTE). As the animal learns the environmental configuration, rats change from deliberative to habitual behavior, and VTE tends to disappear, suggesting a functional relevance in the early stages of learning. Despite the extensive research on spatial navigation, learning and VTE in the rat model, fewer studies have focused on humans. Here, we tested whether head-scanning behaviors that humans typically exhibit during spatial navigation are as predictive of spatial learning as in the rat. Subjects performed a goal-oriented virtual navigation task in a symmetric environment. Spatial learning was assessed through the analysis of trajectories, timings, and head orientations, under habitual and deliberative spatial navigation conditions. As expected, we found that trajectory length and duration decreased with the trial number, implying that subjects learned the spatial configuration of the environment over trials. Interestingly, IdPhi (a standard metric of VTE) also decreased with the trial number, suggesting that humans benefit from the same head-orientation scanning behavior as rats at spatial decision-points. Moreover, IdPhi captured exclusively at the first decision-point of each trial, was correlated with trial trajectory duration and length. Our findings demonstrate that in VTE is a signature of the stage of spatial learning in humans, and can be used to predict performance in navigation tasks with high accuracy.

Keywords: Deliberation, Habitual, Hippocampus, Navigation, Spatial decision-making


Truschzinski, M., Betella, A., Brunnett, G., Verschure, P., (2018). Emotional and cognitive influences in air traffic controller tasks: An investigation using a virtual environment? Applied Ergonomics 69, 1-9

Air traffic controllers are required to perform complex tasks which require attention and high precision. This study investigates how the difficulty of such tasks influences emotional states, cognitive workload and task performance. We use quantitative and qualitative measurements, including the recording of pupil dilation and changes in affect using questionnaires. Participants were required to perform a number of air traffic control tasks using the immersive human accessible Virtual Reality space in the "eXperience Induction Machine". Based on the data collected, we developed and validated a model which integrates personality, workload and affective theories. Our results indicate that the difficulty of an air traffic control task has a direct influence on cognitive workload as well as on the self-reported mood; whereas both mood and workload seem to change independently. In addition, we show that personality, in particular neuroticism, affects both mood and performance of the participants.

Keywords: Air traffic control, Mood, Personality, Virtual reality, Workload


Pacheco, D., Verschure, P. F. M. J., (2018). Long-term spatial clustering in free recall Memory 26, (6), 798–806

We explored the influence of space on the organisation of items in long-term memory. In two experiments, we asked our participants to explore a virtual environment and memorise discrete items presented at specific locations. Memory for those items was later on tested in immediate (T1) and 24 hours delayed (T2) free recall tests, in which subjects were asked to recall as many items as possible in any order. In experiment 2, we further examined the contribution of active and passive navigation in recollection dynamics. Results across experiments revealed a significant tendency for participants to consecutively recall items that were encountered in proximate locations during learning. Moreover, the degree of spatial organisation and the total number of items recalled were positively correlated in the immediate and the delayed tests. Results from experiment 2 indicated that the spatial clustering of items was independent of navigation types. Our results highlight the long-term stability of spatial clustering effects and their correlation with recall performance, complementing previous results collected in immediate or briefly delayed tests.

Keywords: Free recall, Spatial clustering, Spatial memory, Spatial navigation, Virtual reality


Arsiwalla, Xerxes, Signorelli, Camilo M., Puigbo, Jordi-Ysard, Freire, Ismael, Verschure, P., (2018). Are brains computers, emulators or simulators? Biomimetic and Biohybrid Systems 7th International Conference, Living Machines 2018 (Lecture Notes in Computer Science) , Springer International Publishing (Paris, France) 10928, 11-15

There has been intense debate on the question of whether the brain is a computer. If so, that challenge is to show that all cognitive processes can be described by algorithms running on a universal Turing machine. By extension that implies consciousness is a computational process. Both Penrose and Searle have vehemently argued against this view, proposing that consciousness is a fundamentally non-computational process. Even proponents of the brain as a computer metaphor such a Dennett agree that the organizational architecture of the brain is unlike any computing system ever conceived, possibly alluding to non-classical computational processes. The latter class of processes veer away from any program that can be encoded by Church’s lambda calculus. In fact, such a program would have to be based on non-classical logic (either semi-classical or quantum). But quantum logic or machines that might implement them typically are not meant for solving the same type of problems that a classical computer solves (nor are they necessarily faster for any given problem). We will argue that machines implementing non-classical logic might be better suited for simulation rather than computation (a la Turing). It is thus reasonable to pit simulation as an alternative to computation and ask whether the brain, rather than computing, is simulating a model of the world in order to make predictions and guide behavior. If so, this suggests a hardware supporting dynamics more akin to a quantum many-body field theory.


Moulin-Frier, C., Puigbò, J. Y., Arsiwalla, X. D., Sanchez-Fibla, M., Verschure, P., (2018). Embodied artificial intelligence through distributed adaptive control: An integrated framework ICDL-EpiRob 2017 7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics , IEEE (Lisbon, Portugal) , 324-330

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances in the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building on this analysis, we first propose an embodied cognitive architecture integrating heterogeneous subfields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.

Keywords: Cognitive Architectures, Embodied Artificial Intelligence, Evolutionary Arms Race, Unified Theories of Cognition


Santos-Pata, D., Escuredo, A., Mathews, Z., Verschure, P., (2018). Insect behavioral evidence of spatial memories during environmental reconfiguration Biomimetic and Biohybrid Systems 7th International Conference, Living Machines 2018 (Lecture Notes in Computer Science) , Springer International Publishing (Paris, France) 10928, 415-427

Insects are great explorers, able to navigate through long-distance trajectories and successfully find their way back. Their navigational routes cross dynamic environments suggesting adaptation to novel configurations. Arthropods and vertebrates share neural organizational principles and it has been shown that rodents modulate their neural spatial representation accordingly with environmental changes. However, it is unclear whether insects reflexively adapt to environmental changes or retain memory traces of previously explored situations. We sought to disambiguate between insect behavior in environmental novel situations and reconfiguration conditions. An immersive mixed-reality multi-sensory setup was built to replicate multi-sensory cues. We have designed an experimental setup where female crickets Gryllus Bimaculatus were trained to move towards paired auditory and visual cues during primarily phonotactic driven behavior. We hypothesized that insects were capable of identifying sensory modifications in known environments. Our results show that, regardless of the animal’s history, novel situation conditions did not compromise the animals performance and navigational directionality towards a new target location. However, in trials where visual and auditory stimuli were spatially decoupled, the animals heading variability towards a previously known position significantly increased. Our findings showed that crickets can behaviorally manifest environmental reconfiguration, suggesting the encoding for spatial representation.

Keywords: Insect, Memory, Navigation, Spatial representation


Freire, Ismael, Puigbo, J., Arsiwalla, Xerxes, Verschure, Paul, (2018). Modeling the opponent’s action using control-based reinforcement learning Biomimetic and Biohybrid Systems 7th International Conference, Living Machines 2018 (Lecture Notes in Computer Science) , Springer International Publishing (Paris, France) 10928, 179-186

In this paper, we propose an alternative to model-free reinforcement learning approaches that recently have demonstrated Theory-of-Mind like behaviors. We propose a game theoretic approach to the problem in which pure RL has demonstrated to perform below the standards of human-human interaction. In this context, we propose alternative learning architectures that complement basic RL models with the ability to predict the other’s actions. This architecture is tested in different scenarios where agents equipped with similar or varying capabilities compete in a social game. Our different interaction scenarios suggest that our model-based approaches are especially effective when competing against models of equivalent complexity, in contrast to our previous results with more basic predictive architectures. We conclude that the evolution of mechanisms that allow for the control of other agents provide different kinds of advantages that can become significant when interacting with different kinds of agents. We argue that no single proposed addition to the learning architecture is sufficient to optimize performance in these scenarios, but a combination of the different mechanisms suggested is required to achieve near-optimal performance in any case.


Arsiwalla, X. D., Pacheco, D., Principe, A., Rocamora, R., Verschure, P., (2018). A temporal estimate of integrated information for intracranial functional connectivity Artificial Neural Networks and Machine Learning (Lecture Notes in Computer Science) 27th International Conference on Artificial Neural Networks (ICANN 2018) , Springer, Cham (Rhodes, Greece) 11140, 403-412

A major challenge in computational and systems neuroscience concerns the quantification of information processing at various scales of the brain’s anatomy. In particular, using human intracranial recordings, the question we ask in this paper is: How can we estimate the informational complexity of the brain given the complex temporal nature of its dynamics? To address this we work with a recent formulation of network integrated information that is based on the Kullback-Leibler divergence between the multivariate distribution on the set of network states versus the corresponding factorized distribution over its parts. In this work, we extend this formulation for temporal networks and then apply it to human brain data obtained from intracranial recordings in epilepsy patients. Our findings show that compared to random re-wirings of the data, functional connectivity networks, constructed from human brain data, score consistently higher in the above measure of integrated information. This work suggests that temporal integrated information may indeed be a good starting point as a future measure of cognitive complexity.

Keywords: Brain networks, Complexity measures, Computational neuroscience, Functional connectivity


Puigbò, J. Y., Arsiwalla, X. D., Verschure, P., (2018). Challenges of machine learning for living machines Biomimetic and Biohybrid Systems 7th International Conference, Living Machines 2018 (Lecture Notes in Computer Science) , Springer International Publishing (Paris, France) 10928, 382-386

Machine Learning algorithms (and in particular Reinforcement Learning (RL)) have proved very successful in recent years. These have managed to achieve super-human performance in many different tasks, from video-games to board-games and complex cognitive tasks such as path-planning or Theory of Mind (ToM) on artificial agents. Nonetheless, this super-human performance is also super-artificial. Despite some metrics are better than what a human can achieve (i.e. cumulative reward), in less common metrics (i.e. time to learning asymptote) the performance is significantly worse. Moreover, the means by which those are achieved fail to extend our understanding of the human or mammal brain. Moreover, most approaches used are based on black-box optimization, making any comparison beyond performance (e.g. at the architectural level) difficult. In this position paper, we review the origins of reinforcement learning and propose its extension with models of learning derived from fear and avoidance behaviors. We argue that avoidance-based mechanisms are required when training on embodied, situated systems to ensure fast and safe convergence and potentially overcome some of the current limitations of the RL paradigm.

Keywords: Avoidance, Neural networks, Reinforcement learning


Verschure, P., (2018). The architecture of mind and brain Living machines: A handbook of research in biomimetics and biohybrid systems (ed. Prescott, T. J., Lepora, Nathan, Verschure, P.), Oxford Scholarship (Oxford, UK) , 338-345

The components of a Living Machine must be integrated into a functioning whole, which requires a detailed understanding of the architecture of living machines. This chapter starts with a conceptual and historical analysis which from Plato brings us to nineteenth-century neuroscience and early concepts of the layered structure of nervous systems. These concepts were further captured in the cognitive behaviorism of Tolman and came to full fruition in the cognitive revolution of the second half of the twentieth century. Verschure subsequently describes the most relevant proposals of cognitive architectures followed by an overview of the few proposals stemming from modern neuroscience on the architecture of the brain. Subsequently, we will look at contemporary contenders that mediate between cognitive and brain architecture. An important challenge to any model of cognitive architectures is how to benchmark it. Verschure proposes the Unified Theories of Embodied Minds (UTEM) benchmark which advances from Newell’s classic Unified Theories of Cognition benchmark.

Keywords: Architecture, Mind, Brain, Organization, System, Virtualization, Abstraction layers


Pfeifer, R., Verschure, P., (2018). The challenge of autonomous agents: Pitfalls and how to avoid them The Artificial Life Route to Artificial Intelligence (ed. Steels, L., Brooks, R.), Taylor & Francis Group (New York, USA) 9, 237-266

Traditional symbol processing AI has been criticized on many grounds. Well known criticisms concern, among others, brittleness, lack of learning and generalization capacity, lack of fault and noise tolerance, neural implausibility, and the inability to perform in real time. More recently there has been much discussion about situatedness, grounding, and the frame problem.


Verschure, P., (2018). A chronology of Distributed Adaptive Control Living machines: A handbook of research in biomimetics and biohybrid systems (ed. Prescott, T. J., Lepora, Nathan, Verschure, P.), Oxford Scholarship (Oxford, UK) , 346-360

This chapter presents the Distributed Adaptive Control (DAC) theory of the mind and brain of living machines. DAC provides an explanatory framework for biological brains and an integration framework for synthetic ones. DAC builds on several themes presented in the handbook: it integrates different perspectives on mind and brain, exemplifies the synthetic method in understanding living machines, answers well-defined constraints faced by living machines, and provides a route for the convergent validation of anatomy, physiology, and behavior in our explanation of biological living machines. DAC addresses the fundamental question of how a living machine can obtain, retain, and express valid knowledge of its world. We look at the core components of DAC, specific benchmarks derived from the engagement with the physical and the social world (the H4W and the H5W problems) in foraging and human–robot interaction tasks. Lastly we address how DAC targets the UTEM benchmark and the relation with contemporary developments in AI.

Keywords: Distributed Adaptive Control, Problem of priors, Symbol grounding problem, Convergent validation, Foraging, brain, Architecture, system


Vouloutsi, Vasiliki, Verschure, P., (2018). Emotions and self-regulation Living Machines: A Handbook of Research in Biomimetic and Biohybrid Systems (ed. Prescott, T. J., Lepora, Nathan, Verschure, P.), Oxford Scholarship (Oxford, UK) , 327-337

This chapter takes the view that emotions of living machines can be seen from the perspective of self-regulation and appraisal. We will first look at the pragmatic needs to endow machines with emotions and subsequently describe some of the historical background of the science of emotions and its different interpretations and links to affective neuroscience. Subsequently, we argue that emotions can be cast in terms of self-regulation where they provide for a descriptor of the state of the homeostatic processes that maintain the relationship between the agent and its internal and external environment. We augment the notion of homeostasis with that of allostasis which signifies a change from stability through a fixed equilibrium to stability through continuous change. The chapter shows how this view can be used to create complex living machines where emotions are anchored in the need fulfillment of the agent, in this case considering both utilitarian and epistemic needs.

Keywords: Emotion, Motivation, Needs, Appraisal, Self-regulation, Homeostasis, Allostasis, Human–robot interaction, James–Lange theory


Herreros, I., (2018). Learning and control Living machines: A handbook of research in biomimetics and biohybrid systems (ed. Prescott, T. J., Lepora, Nathan, Verschure, P.), Oxford Scholarship (Oxford, UK) , 239-255

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

Keywords: Feedback control, Feed-forward control, Supervised learning, Unsupervised learning, Reinforcement, Learning, Classical conditioning, Operant conditioning, Reflex, Anticipatory reflex


Lepora, Nathan, Verschure, P., Prescott, T. J., (2018). A roadmap for Living Machines research Living machines: A handbook of research in biomimetics and biohybrid systems (ed. Prescott, T. J., Lepora, Nathan, Verschure, P.), Oxford Scholarship (Oxford, UK) , 26-50

This roadmap identifies current trends in biomimetic and biohybrid systems together with their implications for future research and innovation. Important questions include the scale at which these systems are defined, the types of biological systems addressed, the kind of principles sought, the differences between biologically based and biologically inspired approaches, the role in the understanding of living systems, relevant application domains, common benchmarks, the relation to other fields, and developments on the horizon. We interviewed and collated answers from experts who have been involved a series of events organized by the Convergent Science Network. These answers were then collated into themes of research. Overall, we see a field rapidly expanding in influence and impact. As such, this report will provide information to researchers and scientific policy makers on contemporary biomimetics and its future, together with pointers to further reading on relevant topics within this handbook.

Keywords: Biomimetics, Biohybrid, Bio-inspiration, Biologically inspired, Roadmap, Living machines, policy


Verschure, P., (2018). Capabilities Living machines: A handbook of research in biomimetics and biohybrid systems (ed. Prescott, T. J., Lepora, Nathan, Verschure, P.), Oxford Scholarship (Oxford, UK) , 211-217

This chapter introduces the “Capabilities” section of the Handbook of Living Machines. Where the previous section considered building blocks, we recognize that components or modules do not automatically make systems. Hence, in the remainder of this handbook, the emphasis is toward the capabilities of living systems and their emulation in artifacts. Capabilities often arise from the integration of multiple components and thus sensitize us to the need to develop a system-level perspective on living machines. Here we summarize and consider the 14 contributions in this section which cover perception, action, cognition, communication, and emotion, and the integration of these through cognitive architectures into systems that can emulate the full gamut of integrated behaviors seen in animals including, potentially, our own capacity for consciousness.

Keywords: Action, Cognition, Cognitive architecture, Communication, Consciousness, Emotion, Perception


Freire, I. T., Arsiwalla, X. D., Puigbò, J. Y., Verschure, P., (2018). Limits of multi-agent predictive models in the formation of social conventions Frontiers in Artificial Intelligence and Applications (ed. Falomir, Z., Gibert, K., Plaza, E.), IOS Press (Amsterdam, The Netherlands) Volume 308: Artificial Intelligence Research and Development, 297-301

A major challenge in cognitive science and AI is to understand how intelligent agents might be able to predict mental states of other agents during complex social interactions. What are the computational principles of such a Theory of Mind (ToM)? In previous work, we have investigated hypotheses of how the human brain might realize a ToM of other agents in a multi-agent social scenario. In particular, we have proposed control-based cognitive architectures to predict the model of other agents in a game-theoretic task (Battle of the Exes). Our multi-layer architecture implements top-down predictions from adaptive to reactive layers of control and bottom-up error feedback from reactive to adaptive layers. We tested cooperative and competitive strategies among different multi-agent models, demonstrating that while pure RL leads to reasonable efficiency and fairness in social interactions, there are other architectures that can perform better in specific circumstances. However, we found that even the best predictive models fall short of human data in terms of stability of social convention formation. In order to explain this gap between humans and predictive AI agents, in this work we propose introducing the notion of trust in the form of mutual agreements between agents that might enhance stability in the formation of conventions such as turn-taking.

Keywords: Cognitive Architectures, Game Theory, Multi-Agent Models, Reinforcement Learning, Theory of Mind


Verschure, P., Prescott, T. J., (2018). A living machines approach to the sciences of mind and brain Living Machines: A Handbook of Research in Biomimetic and Biohybrid Systems (ed. Prescott, T. J., Lepora, Nathan, Verschure, P.), Oxford Scholarship (Oxford, UK) , 15-25

How do the sciences of mind and brain—neuroscience, psychology, cognitive science, and artificial intelligence (AI)—stand in relation to each other in the 21st century? This chapter proposes that despite our knowledge expanding at ever-accelerating rates, our understanding of the relationship between mind and brain is, in some important sense, becoming less and less. An increasing explanatory gap can only be bridged by a multi-tiered and integrated theoretical framework that recognizes the value of developing explanations at different levels, combining these into cross-level integrated theories, and directly contributing to new technologies that improve the human condition. Development of technologies that instantiate principles gleaned from the study of the mind and brain, or biomimetic technologies, is a key part of the validation process for scientific theories of mind and brain. We call this strategy for the integration of science and engineering a Living Machines approach. Following this path can lead not only to better science, and useful engineering, but also a richer view of human experience and of relationships between science, engineering, and art.

Keywords: Convergent validation, Multi-tiered theories, Paradigms in cognitive science, Philosophy of science, Physical models, Reductionism


Prescott, T. J., Verschure, P. F. M. J., (2018). Living machines: An introduction Living Machines: A Handbook of Research in Biomimetic and Biohybrid Systems (ed. Prescott, T. J., Lepora, Nathan, Verschure, P.), Oxford Scholarship (Oxford, UK) , 3-14

Biomimetics is the development of novel technologies through the distillation of principles from the study of biological systems. Biohybrid systems are formed by at least one biological component—an already existing living system—and at least one artificial, newly engineered component. The development of either biomimetic or biohybrid systems requires a deep understanding of the operation of living systems, and the two fields are united under the theme of “living machines”—the idea that we can construct artifacts that not only mimic life but share some of the same fundamental principles. This chapter sets out the philosophy and history underlying this Living Machines approach and sets the scene for the remainder of this book.

Keywords: Biohybrids, Biological principles, Biomimetics, History of technology, Living machines, Technology ethics


Vouloutsi, V., Halloy, J., Mura, A., Mangan, M., Lepora, N., Prescott, T. J., Verschure, P., (2018). Preface Biomimetic and Biohybrid Systems (ed. Vouloutsi, Vasiliki, Halloy, José, Mura, Anna, Mangan, Michael, Lepora, Nathan, Prescott, T. J., Verschure, P.), Springer International Publishing (Lausanne, Switzerland) 10928, V-VII

Arsiwalla, X. D., Signorelli, C. M., Puigbo, J. Y., Freire, I. T., Verschure, P., (2018). What is the physics of intelligence? Frontiers in Artificial Intelligence and Applications (ed. Falomir, Z., Gibert, K., Plaza, E.), IOS Press (Amsterdam, The Netherlands) Volume 308: Artificial Intelligence Research and Development, 283-286

In the absence of a first-principles definition, the concept of intelligence is often specified in terms of its phenomenological functions as a capacity or ability to solve problems autonomously. Whenever an agent, biological or artificial, possesses this ability, it is considered intelligent, otherwise not. While this description serves as a useful correlate of intelligence, it is far from a principled explanation that provides a general, yet precise definition along with predictions of mechanisms leading to intelligent behavior. We do not want an explanation to depend on any functionality that itself might be a consequence of intelligence. A possible conceptualization of a function-free approach might be to formulate the concept in terms of dynamical information complexity. This constitute a first step towards a statistical mechanics theory of intelligence. In this paper, we outline the steps towards a physics-based definition of intelligence.

Keywords: Complexity, Information Theory, Physics of Intelligence


Vouloutsi, Vasiliki, Halloy, José, Mura, Anna, Mangan, Michael, Lepora, Nathan, Prescott, T. J., Verschure, P., (2018). Biomimetic and Biohybrid Systems 7th International Conference, Living Machines 2018, Paris, France, July 17–20, 2018, Proceedings , Springer International Publishing (Lausanne, Switzerland) 10928, 1-551

This book constitutes the proceedings of the 7th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2018, held in Paris, France, in July 2018. The 40 full and 18 short papers presented in this volume were carefully reviewed and selected from 60 submissions. The theme of the conference targeted at the intersection of research on novel life-like technologies inspired by the scientific investigation of biological systems, biomimetics, and research that seeks to interface biological and artificial systems to create biohybrid systems.

Keywords: Artificial neural network, Bio-actuators, Bio-robotics, Biohybrid systems, Biomimetics, Bipedal robots, Earthoworm-like robots, Robotics, Decision-making, Tactile sensing, Soft robots, Locomotion, Insects, Sensors, Actuators, Robots, Artificial intelligence, Neural networks, Motion planning, Learning algorithms


Prescott, T. J., Lepora, Nathan, Verschure, P., (2018). Living machines: A handbook of research in biomimetics and biohybrid systems Oxford Scholarship , 1-623

Biomimetics is the development of novel technologies through the distillation of ideas from the study of biological systems. Biohybrids are formed through the combination of at least one biological component—an existing living system—and at least one artificial, newly engineered component. These two fields are united under the theme of Living Machines—the idea that we can construct artifacts that not only mimic life but also build on the same fundamental principles. The research described in this volume seeks to understand and emulate life’s ability to self-organize, metabolize, grow, and reproduce; to match the functions of living tissues and organs such as muscles, skin, eyes, ears, and neural circuits; to replicate cognitive and physical capacities such as perception, attention, locomotion, grasp, emotion, and consciousness; and to assemble all of these elements into integrated systems that can hold a technological mirror to life or that have the capacity to merge with it. We conclude with contributions from philosophers, ethicists, and futurists on the potential impacts of this remarkable research on society and on how we see ourselves.

Keywords: Novel technologies, Biomimetics, Biohybrids, Living systems, Living machines, Biological principles, Technology ethics, Societal impacts


Maffei, Giovanni, Herreros, Ivan, Sanchez-Fibla, Marti, Friston, Karl J., Verschure, Paul F. M. J., (2017). The perceptual shaping of anticipatory actions Proceedings of the Royal Society B , 284, (1869)

Humans display anticipatory motor responses to minimize the adverse effects of predictable perturbations. A widely accepted explanation for this behavior relies on the notion of an inverse model that, learning from motor errors, anticipates corrective responses. Here, we propose and validate the alternative hypothesis that anticipatory control can be realized through a cascade of purely sensory predictions that drive the motor system, reflecting the causal sequence of the perceptual events preceding the error. We compare both hypotheses in a simulated anticipatory postural adjustment task. We observe that adaptation in the sensory domain, but not in the motor one, supports the robust and generalizable anticipatory control characteristic of biological systems. Our proposal unites the neurobiology of the cerebellum with the theory of active inference and provides a concrete implementation of its core tenets with great relevance both to our understanding of biological control systems and, possibly, to their emulation in complex artefacts.

Keywords: Active inference, Cerebellum, Computational model, Motor control, Perceptual learning


Pacheco, D., Sánchez-Fibla, M., Duff, A., Verschure, P. F. M. J., (2017). A spatial-context effect in recognition memory Frontiers in Behavioral Neuroscience 11, Article 143

We designed a novel experiment to investigate the modulation of human recognition memory by environmental context. Human participants were asked to navigate through a four-arm Virtual Reality (VR) maze in order to find and memorize discrete items presented at specific locations in the environment. They were later on tested on their ability to recognize items as previously presented or new. By manipulating the spatial position of half of the studied items during the testing phase of our experiment, we could assess differences in performance related to the congruency of environmental information at encoding and retrieval. Our results revealed that spatial context had a significant effect on the quality of memory. In particular, we found that recognition performance was significantly better in trials in which contextual information was congruent as opposed to those in which it was different. Our results are in line with previous studies that have reported spatial-context effects in recognition memory, further characterizing their magnitude under ecologically valid experimental conditions.

Keywords: Context effects, Recognition memory, Spatial behavior, Spatial memory and navigation, Virtual reality


Hindriks, Rikkert, Schmiedt, Joscha, Arsiwalla, Xerxes D., Peter, Alina, Verschure, Paul F. M. J., Fries, Pascal, Schmid, Michael C., Deco, Gustavo, (2017). Linear distributed source modeling of local field potentials recorded with intra-cortical electrode arrays PLoS ONE 12, (12), e0187490

Planar intra-cortical electrode (Utah) arrays provide a unique window into the spatial organization of cortical activity. Reconstruction of the current source density (CSD) underlying such recordings, however, requires “inverting” Poisson’s equation. For inter-laminar recordings, this is commonly done by the CSD method, which consists in taking the second-order spatial derivative of the recorded local field potentials (LFPs). Although the CSD method has been tremendously successful in mapping the current generators underlying inter-laminar LFPs, its application to planar recordings is more challenging. While for inter-laminar recordings the CSD method seems reasonably robust against violations of its assumptions, is it unclear as to what extent this holds for planar recordings. One of the objectives of this study is to characterize the conditions under which the CSD method can be successfully applied to Utah array data. Using forward modeling, we find that for spatially coherent CSDs, the CSD method yields inaccurate reconstructions due to volume-conducted contamination from currents in deeper cortical layers. An alternative approach is to “invert” a constructed forward model. The advantage of this approach is that any a priori knowledge about the geometrical and electrical properties of the tissue can be taken into account. Although several inverse methods have been proposed for LFP data, the applicability of existing electroencephalographic (EEG) and magnetoencephalographic (MEG) inverse methods to LFP data is largely unexplored. Another objective of our study therefore, is to assess the applicability of the most commonly used EEG/MEG inverse methods to Utah array data. Our main conclusion is that these inverse methods provide more accurate CSD reconstructions than the CSD method. We illustrate the inverse methods using event-related potentials recorded from primary visual cortex of a macaque monkey during a motion discrimination task.


Ballester, Rubio Belén, Nirme, Jens, Camacho, Irene, Duarte, Esther, Rodríguez, Susana, Cuxart, Ampar, Duff, Armin, Verschure, F. M. J. Paul, (2017). Domiciliary VR-based therapy for functional recovery and cortical reorganization: Randomized controlled trial in participants at the chronic stage post stroke JMIR Serious Games , 5, (3), e15

Background: Most stroke survivors continue to experience motor impairments even after hospital discharge. Virtual reality-based techniques have shown potential for rehabilitative training of these motor impairments. Here we assess the impact of at-home VR-based motor training on functional motor recovery, corticospinal excitability and cortical reorganization. Objective: The aim of this study was to identify the effects of home-based VR-based motor rehabilitation on (1) cortical reorganization, (2) corticospinal tract, and (3) functional recovery after stroke in comparison to home-based occupational therapy. Methods: We conducted a parallel-group, controlled trial to compare the effectiveness of domiciliary VR-based therapy with occupational therapy in inducing motor recovery of the upper extremities. A total of 35 participants with chronic stroke underwent 3 weeks of home-based treatment. A group of subjects was trained using a VR-based system for motor rehabilitation, while the control group followed a conventional therapy. Motor function was evaluated at baseline, after the intervention, and at 12-weeks follow-up. In a subgroup of subjects, we used Navigated Brain Stimulation (NBS) procedures to measure the effect of the interventions on corticospinal excitability and cortical reorganization. Results: Results from the system?s recordings and clinical evaluation showed significantly greater functional recovery for the experimental group when compared with the control group (1.53, SD 2.4 in Chedoke Arm and Hand Activity Inventory). However, functional improvements did not reach clinical significance. After the therapy, physiological measures obtained from a subgroup of subjects revealed an increased corticospinal excitability for distal muscles driven by the pathological hemisphere, that is, abductor pollicis brevis. We also observed a displacement of the centroid of the cortical map for each tested muscle in the damaged hemisphere, which strongly correlated with improvements in clinical scales. Conclusions: These findings suggest that, in chronic stages, remote delivery of customized VR-based motor training promotes functional gains that are accompanied by neuroplastic changes. Trial Registration: International Standard Randomized Controlled Trial Number NCT02699398 (Archived by ClinicalTrials.gov at https://clinicaltrials.gov/ct2/show/NCT02699398?term=NCT02699398&rank=1)

Keywords: Stroke, Movement disorder, Recovery of function, neuroplasticity, Transcranial magnetic stimulation, Physical therapy, Hemiparesis, Computer applications software


Santos-Pata, D., Zucca, R., Low, S. C., Verschure, P. F. M. J., (2017). Size matters: How scaling affects the interaction between grid and border cells Frontiers in Computational Neuroscience , 11, Article 65

Many hippocampal cell types are characterized by a progressive increase in scale along the dorsal-to-ventral axis, such as in the cases of head-direction, grid and place cells. Also located in the medial entorhinal cortex (MEC), border cells would be expected to benefit from such scale modulations. However, this phenomenon has not been experimentally observed. Grid cells in the MEC of mammals integrate velocity related signals to map the environment with characteristic hexagonal tessellation patterns. Due to the noisy nature of these input signals, path integration processes tend to accumulate errors as animals explore the environment, leading to a loss of grid-like activity. It has been suggested that border-to-grid cells' associations minimize the accumulated grid cells' error when rodents explore enclosures. Thus, the border-grid interaction for error minimization is a suitable scenario to study the effects of border cell scaling within the context of spatial representation. In this study, we computationally address the question of (i) border cells' scale from the perspective of their role in maintaining the regularity of grid cells' firing fields, as well as (ii) what are the underlying mechanisms of grid-border associations relative to the scales of both grid and border cells. Our results suggest that for optimal contribution to grid cells' error minimization, border cells should express smaller firing fields relative to those of the associated grid cells, which is consistent with the hypothesis of border cells functioning as spatial anchoring signals.

Keywords: Border cells, Error minimization, Grid cells, Navigation, Path integration


Puigbò, Jordi-Ysard, Gonzalez-Ballester, Miguel Ángel, Verschure, Paul , (2017). Behavior-state dependent modulation of perception based on a model of conditioning Biomimetic and Biohybrid Systems 6th International Conference, Living Machines 2017 (Lecture Notes in Computer Science) , Springer International Publishing (Standfor, USA) 10384, 387-393

The embodied mammalian brain evolved to adapt to an only partially known and knowable world. The adaptive labeling of the world is critically dependent on the neocortex which in turn is modulated by a range of subcortical systems such as the thalamus, ventral striatum and the amygdala. A particular case in point is the learning paradigm of classical conditioning where acquired representations of states of the world such as sounds and visual features are associated with predefined discrete behavioral responses such as eye blinks and freezing. Learning progresses in a very specific order, where the animal first identifies the features of the task that are predictive of a motivational state and then forms the association of the current sensory state with a particular action and shapes this action to the specific contingency. This adaptive feature selection has both attentional and memory components, i.e. a behaviorally relevant state must be detected while its representation must be stabilized to allow its interfacing to output systems. Here we present a computational model of the neocortical systems that underlie this feature detection process and its state dependent modulation mediated by the amygdala and its downstream target, the nucleus basalis of Meynert. Specifically, we analyze how amygdala driven cholinergic modulation these mechanisms through computational modeling and present a framework for rapid learning of behaviorally relevant perceptual representations.


(See full publication list in ORCID)

Equipment

  • EXperience Induction Machine (XIM), an immersive room equipped with a number of sensors and effectors that have been constructed to conduct experiments in mixed-reality.
  • Robotics lab
  • Codi-Bot, the musical robot that teaches you how to program
  • iqr: simulator for large-scale neural systems
  • Collective machine cognition: Autonomous dynamic mapping and planning using a hybrid team of aerial and ground-based robots
  • Humanoid robots: iCub
  • Quality of Life Technologies

Collaborations

In 2014, SPECS created the spin-off company “Eodyne“.

  • Terrence W. Deacon
    UC Berkeley, US
  • Andreas K. Engel 
    Neurophysiology, UKE, Hamburg, DE
  • Karl Friston
     University College London, UK
  • Bill S Hansson
    Max Planck Instit., DE
  • Jonatas Manzolli
    Unicamp Univ., Brazil
  • Tony Prescott
    Sheffield Univ.
  • errSejnowski 
    Salk Institute, San Diego, US

IBEC formalized collaborations 

  1. Collaboration/agreement with UCL for data sharing with Nick Warden of University College London (UCL) and Belen Rubio Ballester (SPECS) 
  2. Collaboration/agreement withRuhr- Universität Bochum Institut für Kognitive Neurowissenschaft for Daniel Pacheco visiting scientist position at IBEC 
  3. Collaboration/agreement withthe Faculty of Social and Behavioral Sciences at the University of Amsterdam and Maria Blancas for a study with children on pedagogical learning. 

Clinical collaborations

  • The epileptic unit –Hospital Del Mar 
  • Rehabilitation (Stroke) Unit – Val D’Hebron 
  • Cannabis addiction – Hospital Clinic 
  • Stroke Aphasia – Hospital Tarragona 
  • Stroke/Schizophrenia/Mental health: Hospital SJDD-mental  
  • Personality Disorders –Hospital San Raffaele (Italy) 
  • Stroke – Hospital Del Ma 
  • Vestibular study –Hospital Tarragona 
  • Rehyb project: Valduce Hospital (Italy), Schön Klinik (Germany) 
  • Alcohol addiction Hospital Lindow (Germany) 

News/Jobs

Researchers from the IBEC develop a Virtual Reality system to treat speech disorders

Researchers from the IBEC have developed a virtual reality-based system for rehabilitating patients with Broca’s aphasia. RGSa has been proven to improve communicative frequency and effectiveness in daily life, as well as sustaining improvements in testing after an 8-week period.

Rehabilitation to recover speech after brain damage is efficient, provided that it is carried out intensively, and can be included in relevant behavioural tasks. However, limited resources in healthcare systems cannot always provide said treatment in sufficient doses. Achieving a cost-effective, evidence-based rehabilitation method is one of the objectives targeted by the SPECS research group.

Read more…

How perception shapes our actions

Last Saturday, another “Classico” saw Messi and Ronaldo display their other-worldly skills and ball control. At the heart of their performance stands the amazing ability to control their bodies in anticipation of the movements of their team members, opponents – and especially the football.

These anticipatory motor actions are essential for sport, but also underlie our everyday behavior, from walking or grasping to riding a bicycle or typing on a keyboard. But how exactly are these actions controlled?

Read more…