Journal ArticleDOI
Cholinergic Behavior State-Dependent Mechanisms of Neocortical Gain Control: a Neurocomputational Study.
Jordi-Ysard Puigbo,Giovanni Maffei,Ivan Herreros,Mario Ceresa,M. A. González Ballester,Paul F. M. J. Verschure +5 more
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TLDR
In this model, 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.Abstract:
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.read more
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Book ChapterDOI
Neurophysiological Model of Migraine Pathophysiology: Bringing the Past into the Future
Gianluca Coppola,Francesco Pierelli,Jean Schoenen,Shuu Jiun Wang,Shuu Jiun Wang,Wei Ta Chen,Wei Ta Chen +6 more
TL;DR: This book has summarized how the migraine brain has been explored with available neurophysiological methods and thinks it is time to define migraine as a ‘biobehavioural organic maladaptive central pain’.
Journal ArticleDOI
Switching Operation Modes in the Neocortex via Cholinergic Neuromodulation : A Computational Model of Uncertainty, Learning, and Inhibition.
Jordi-Ysard Puigbo,Xerxes D. Arsiwalla,Miguel Ángel González-Ballester,Paul F. M. J. Verschure +3 more
TL;DR: This model proposes that cortical acetylcholine favors sensory exploration over exploitation in a cortical microcircuit dedicated to estimating sensory uncertainty, and identifies its interactions with cortical inhibitory interneurons and derives a biophysically grounded computational model able to capture and learn from uncertainty.
Journal ArticleDOI
Multi-modal and multi-model interrogation of large-scale functional brain networks
Francesca Castaldo,Francisco Páscoa dos Santos,R. Timms,J. Cabral,Jakub Vohryzek,Gustavo Deco,Mark W. Woolrich,Karl J. Friston,Paul F. M. J. Verschure,Vladimir Litvak +9 more
TL;DR: In this paper , the authors propose to link distinct features of brain activity captured across modalities to the dynamics unfolding on a macroscopic structural connectome, and demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delaycoupled oscillators at 40 Hz.
Book ChapterDOI
Challenges of Machine Learning for Living Machines
TL;DR: It is argued 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.
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