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Dynamic branching in a neural network model for probabilistic prediction of sequences

Abstract : An important function of the brain is to adapt behavior by selecting between different predictions of sequences of stimuli likely to occur in the environment. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, both analytically and through simulations. Results show how synaptic efficacy, retroactive inhibition and short-term synaptic depression determine the dynamics of choices between different predictions of sequences having different probabilities. Further results show that changes in the probability of the different predictions depend on variations of neuronal gain. Such variations allow the network to optimize the probability of its predictions to changing probabilities of the sequences without changing synaptic efficacy.
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Contributor : Pascal Chossat Connect in order to contact the contributor
Submitted on : Tuesday, January 18, 2022 - 3:33:54 PM
Last modification on : Tuesday, March 8, 2022 - 2:24:00 PM


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  • HAL Id : hal-03532787, version 1


Elif Köksal Ersöz, Pascal Chossat, Martin Krupa, Frédéric Lavigne. Dynamic branching in a neural network model for probabilistic prediction of sequences. 2022. ⟨hal-03532787⟩



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