Shared input and recurrency in neural networks for metabolically efficient information transmission

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985823%3A_____%2F24%3A00584159" target="_blank" >RIV/67985823:_____/24:00584159 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1371/journal.pcbi.1011896" target="_blank" >https://doi.org/10.1371/journal.pcbi.1011896</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pcbi.1011896" target="_blank" >10.1371/journal.pcbi.1011896</a>

Result language

angličtina

Original language name

Shared input and recurrency in neural networks for metabolically efficient information transmission

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

PLoS Computational Biology

ISSN

1553-734X

e-ISSN

1553-7358

Volume of the periodical

20

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

23

Pages from-to

e1011896

UT code for WoS article

001172954600003

EID of the result in the Scopus database

2-s2.0-85185777829