Dynamic branching in a neural network model for probabilistic prediction of sequences - Université de Rennes Accéder directement au contenu
Article Dans Une Revue Journal of Computational Neuroscience Année : 2022

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

Résumé

An important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. 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 selection between different branches predicting sequences of stimuli of 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.
Fichier principal
Vignette du fichier
article_EPMF.pdf (2.36 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03532787 , version 1 (18-01-2022)

Identifiants

Citer

Elif Köksal Ersöz, Pascal Chossat, Martin Krupa, Frédéric Lavigne. Dynamic branching in a neural network model for probabilistic prediction of sequences. Journal of Computational Neuroscience, 2022, 50 (4), pp.537-557. ⟨10.1007/s10827-022-00830-y⟩. ⟨hal-03532787⟩
150 Consultations
160 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More