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Belkaid, M.

Publications and source records attributed to Belkaid, M..

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Modeling Uncertainty-Seeking Behavior Mediated by Cholinergic Influence on Dopamine

Recent findings suggest that acetylcholine mediates uncertainty-seeking behaviors through its projection to dopamine neurons - another neuromodulatory system known for its major implication in reinforcement learning and decision-making. In this paper, we propose a leaky-integrate-and-fire model of this mechanism. It implements a softmax-like selection with an uncertainty bonus by a cholinergic drive to dopaminergic neurons, which in turn influence synaptic currents of downstream neurons. The model is able to reproduce experimental data in two decision-making tasks. It also predicts that i) in the absence of cholinergic input, dopaminergic activity would not correlate with uncertainty, and that ii) the adaptive advantage brought by the implemented uncertainty-seeking mechanism is most useful when sources of reward are not highly uncertain. Moreover, this modeling work allows us to propose novel experiments which might shed new light on the role of acetylcholine in both random and directed exploration. Overall, this study thus contributes to a more comprehensive understanding of the roles of the cholinergic system and its involvement in decision-making in particular.

neuroscience

Mice adaptively generate choice variability in a deterministic task

Can our choices just be driven by chance? To investigate this question, we designed a deterministic setting in which mice reinforce non-repetitive choice sequences, and modeled it using reinforcement learning. Mice progressively increased their choice variability using a memory-free, pseudo-random selection, rather than by learning complex sequences. Our results demonstrate that a decision-making process can self-generate variability and randomness even when the rules governing reward delivery are not stochastic.

neuroscience