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Shared input and recurrency in neural networks for metabolically efficient information transmission
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SYSNO ASEP 0584159 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Shared input and recurrency in neural networks for metabolically efficient information transmission Tvůrce(i) Bárta, Tomáš (FGU-C) RID, ORCID
Košťál, Lubomír (FGU-C) RID, ORCID, SAIČíslo článku e1011896 Zdroj.dok. PLoS Computational Biology - ISSN 1553-734X
Roč. 20, č. 2 (2024)Poč.str. 23 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova neural networks ; probability distribution ; neurons Obor OECD Statistics and probability Způsob publikování Open access Institucionální podpora FGU-C - RVO:67985823 UT WOS 001172954600003 EID SCOPUS 85185777829 DOI 10.1371/journal.pcbi.1011896 Anotace Shared input to a population of neurons induces noise correlations, which can decrease the information carried by a population activity. Inhibitory feedback in recurrent neural networks can reduce the noise correlations and thus increase the information carried by the population activity. However, the activity of inhibitory neurons is costly. This inhibitory feedback decreases the gain of the population. Thus, depolarization of its neurons requires stronger excitatory synaptic input, which is associated with higher ATP consumption. Given that the goal of neural populations is to transmit as much information as possible at minimal metabolic costs, it is unclear whether the increased information transmission reliability provided by inhibitory feedback compensates for the additional costs. We analyze this problem in a network of leaky integrate-and-fire neurons receiving correlated input. By maximizing mutual information with metabolic cost constraints, we show that there is an optimal strength of recurrent connections in the network, which maximizes the value of mutual information-per-cost. For higher values of input correlation, the mutual information-per-cost is higher for recurrent networks with inhibitory feedback compared to feedforward networks without any inhibitory neurons. Our results, therefore, show that the optimal synaptic strength of a recurrent network can be inferred from metabolically efficient coding arguments and that decorrelation of the input by inhibitory feedback compensates for the associated increased metabolic costs. Pracoviště Fyziologický ústav Kontakt Lucie Trajhanová, lucie.trajhanova@fgu.cas.cz, Tel.: 241 062 400 Rok sběru 2025 Elektronická adresa https://doi.org/10.1371/journal.pcbi.1011896
Počet záznamů: 1