Staff member


Paul Verschure

Group Leader / ICREA Research Professor
Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS)
pverschure@ibecbarcelona.eu

Staff member publications

Puigbò, J. Y., Maffei, G., Herreros, I., Ceresa, M., González Ballester, M. A., Verschure, P. F. M. J., (2017). Cholinergic behavior state-dependent mechanisms of neocortical gain control: A neurocomputational study Molecular Neurobiology in press

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


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., (2017). 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 in press

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., 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


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


Marco, S., Gutiérrez-Gálvez, A., Lansner, A., Martinez, D., Rospars, J. P., Beccherelli, R., Perera, A., Pearce, T. C., Verschure, P. F. M. J., Persaud, K., (2014). A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation Microsystem Technologies 20, (4-5), 729-742

Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, in a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy and efficient combinatorial coding, with unmatched chemical information processing mechanisms. The last decade has seen important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. The EU-funded Project NEUROCHEM (Bio-ICT-FET- 216916) developed novel computing paradigms and biologically motivated artefacts for chemical sensing, taking its inspiration from the biological olfactory pathway. To demonstrate this approach, a biomimetic demonstrator has been built that features a very large-scale sensor array (65,536 elements) using conducting polymer technology which mimics the olfactory receptor neuron layer. It implements derived computational neuroscience algorithms in an embedded system that interfaces the chemical sensors and processes their signals in real-time. This embedded system integrates abstracted computational models of the main anatomic building blocks in the olfactory pathway: the olfactory bulb, and olfactory cortex in vertebrates (respectively, antennal lobe and mushroom bodies in the insect). For implementation in the embedded processor, an abstraction phase has been carried out in which their processing capabilities are captured by algorithmic solutions implemented in software. Finally, the algorithmic models are tested in mixed chemical plumes with an odour robot having navigation capabilities.


Ziyatdinov, Andrey, Calvo, Jose Maria Blanco, Lechon, Miguel, Bermudez i Badia, Sergi, Verschure, Paul F. M. J., Marco, Santiago, Perera, Alexandre, (2011). Odour mapping under strong backgrounds with a metal oxide sensor array Olfaction and Electronic Nose: Proceedings of the 14th International Symposium on Olfaction and Electronic Nose AIP Conference Proceedings (ed. Perena Gouma, SUNY Stony Brook), AIP (New York City, USA) 1362, (1), 232-233

This work describes the data from navigation experiments with the mobile robot, equipped with the sensor array of three MOX gas sensors. Performed four series of measurements aim to explore the capabilities of sensor array to build the odour map with one or two odour sources in the wind tunnel space. It was demonstrated that the method based on Independent Component Analysis (ICA) is able to discriminate two odour sources, that in future can be used in the surge-and-cast robot navigation algorithm.

Keywords: Mobile robots, Data acquisition, MIS devices, Chemioception


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