Publications

Abstract Critical dynamics, characterized by scale-free neuronal avalanches, is thought to underlie optimal function in the sensory cortices by maximizing information transmission, capacity, and dynamic range. In contrast, deviations from criticality have not yet been considered to support any cognitive processes. Nonetheless, neocortical areas related to working memory and decision-making seem to rely on long-lasting periods of ignition-like persistent firing. Such firing patterns are reminiscent of supercritical states where runaway excitation dominates the circuit dynamics. In addition, a macroscopic gradient of the relative density of Somatostatin (SST+) and Parvalbumin (PV+) inhibitory interneurons throughout the cortical hierarchy has been suggested to determine the functional specialization of low- versus high-order cortex. These observations thus raise the question of whether persistent activity in high-order areas results from the intrinsic features of the neocortical circuitry. We used an attractor model of the canonical cortical circuit performing a perceptual decision-making task to address this question. Our model reproduces the known saddle-node bifurcation where persistent activity emerges, merely by increasing the SST+/PV+ ratio while keeping the input and recurrent excitation constant. The regime beyond such a phase transition renders the circuit increasingly sensitive to random fluctuations of the inputs -i.e., chaotic-, defining an optimal SST+/PV+ ratio around the edge-of-chaos. Further, we show that both the optimal SST+/PV+ ratio and the region of the phase transition decrease monotonically with increasing input noise. This suggests that cortical circuits regulate their intrinsic dynamics via inhibitory interneurons to attain optimal sensitivity in the face of varying uncertainty. Hence, on the one hand, we link the emergence of supercritical dynamics at the edge-of-chaos to the gradient of the SST+/PV+ ratio along the cortical hierarchy, and, on the other hand, explain the behavioral effects of the differential regulation of SST+ and PV+ interneurons by neuromodulators like acetylcholine in the presence of input uncertainty.

JTD Keywords: attractor model, cortex, cortical networks, edge-of-chaos, model, nmda receptors, Attractor model, Cortical hierarchies, Decision making, Dynamics, Edge of chaos, Edge-of-chaos, High-order, Higher-order, Inhibitory interneurons, Neurons, Optimal decision making, Persistent activities, Persistent activity, Supercritical, Supercriticality

The ability to control neural activity is essential for research not only in basic neuroscience, as spatiotemporal control of activity is a fundamental experimental tool, but also in clinical neurology for therapeutic brain interventions. Transcranial-magnetic, ultrasound, and alternating/direct current (AC/DC) stimulation are some available means of spatiotemporal controlled neuromodulation. There is also light-mediated control, such as optogenetics, which has revolutionized neuroscience research, yet its clinical translation is hampered by the need for gene manipulation. As a drug-based light-mediated control, the effect of a photoswitchable muscarinic agonist (Phthalimide-Azo-Iper (PAI)) on a brain network is evaluated in this study. First, the conditions to manipulate M2 muscarinic receptors with light in the experimental setup are determined. Next, physiological synchronous emergent cortical activity consisting of slow oscillations-as in slow wave sleep-is transformed into a higher frequency pattern in the cerebral cortex, both in vitro and in vivo, as a consequence of PAI activation with light. These results open the way to study cholinergic neuromodulation and to control spatiotemporal patterns of activity in different brain states, their transitions, and their links to cognition and behavior. The approach can be applied to different organisms and does not require genetic manipulation, which would make it translational to humans.

JTD Keywords: brain states, light-mediated control, muscarinic acetylcholine receptors, neuromodulation, Activation, Alternating/direct currents, Basal forebrain, Brain, Brain states, Clinical research, Clinical translation, Controlled drug delivery, Cortex, Forebrain cholinergic system, Genetic manipulations, Higher frequencies, Hz oscillation, Light‐, Light-mediated control, Mediated control, Muscarinic acetylcholine receptors, Muscarinic agonists, Muscarinic receptor, Neurology, Neuromodulation, Neurons, Noradrenergic modulation, Parvalbumin-positive interneurons, Photopharmacology, Receptor-binding, Slow, Spatiotemporal control, Spatiotemporal patterns

Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training ANN using error backpropagation has created the current revolution in artificial intelligence, raising the question of whether the epistemic autonomy displayed in biological cognition can be achieved with error backpropagation-based learning. We present evidence suggesting that the entorhinal–hippocampal complex combines epistemic autonomy with error backpropagation. Specifically, we propose that the hippocampus minimizes the error between its input and output signals through a modulatory counter-current inhibitory network. We further discuss the computational emulation of this principle and analyze it in the context of autonomous cognitive systems. © 2021 Elsevier Ltd

JTD Keywords: computational model, dentate gyrus, error backpropagation, granule cells, grid cells, hippocampus, inhibition, input, neural-networks, neurons, transformation, Artificial intelligence, Artificial neural network, Back propagation, Backpropagation, Brain, Cognitive systems, Counter current, Error back-propagation, Error backpropagation, Errors, Expressing interneurons, Hippocampal complex, Hippocampus, Human experiment, Input and outputs, Learning, Mammal, Mammalian hippocampus, Mammals, Neural networks, Nonhuman, Review, Self-supervised learning

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.

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

The term ‘prion-like’ is used to define some misfolded protein species that propagate intercellularly, triggering protein aggregation in recipient cells. For cell binding, both direct plasma membrane interaction and membrane receptors have been described for particular amyloids. In this respect, emerging evidence demonstrates that several β-sheet enriched proteins can bind to the cellular prion protein (PrPC). Among other interactions, the physiological relevance of the binding between β-amyloid and PrPC has been a relevant focus of numerous studies. At the molecular level, published data point to the second charged cluster domain of the PrPC molecule as the relevant binding domain of the β-amyloid/PrPC interaction. In addition to β-amyloid, participation of PrPC in binding α-synuclein, responsible for neurodegenerative synucleopathies, has been reported. Although results indicate relevant participation of PrPC in the spreading of α-synuclein in living mice, the physiological relevance of the interaction remains elusive. In this comment, we focus our attention on summarizing current knowledge of PrPC as a receptor for amyloid proteins and its physiological significance, with particular focus on α-synuclein.

JTD Keywords: α-synuclein, Charged cluster domain, Interneuronal transport, LAG3, Neurodegeneration, PrPC, Parkinson disease