The effect of inhibition on rate code efficiency indicators
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985823%3A_____%2F19%3A00518628" target="_blank" >RIV/67985823:_____/19:00518628 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1371/journal.pcbi.1007545" target="_blank" >https://doi.org/10.1371/journal.pcbi.1007545</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1371/journal.pcbi.1007545" target="_blank" >10.1371/journal.pcbi.1007545</a>
Alternative languages
Result language
angličtina
Original language name
The effect of inhibition on rate code efficiency indicators
Original language description
In this paper we investigate the rate coding capabilities of neurons whose input signal are alterations of the base state of balanced inhibitory and excitatory synaptic currents. We consider different regimes of excitation-inhibition relationship and an established conductance-based leaky integrator model with adaptive threshold and parameter sets recreating biologically relevant spiking regimes. We find that given mean post-synaptic firing rate, counter-intuitively, increased ratio of inhibition to excitation generally leads to higher signal to noise ratio (SNR). On the other hand, the inhibitory input significantly reduces the dynamic coding range of the neuron. We quantify the joint effect of SNR and dynamic coding range by computing the metabolic efficiency—the maximal amount of information per one ATP molecule expended (in bits/ATP). Moreover, by calculating the metabolic efficiency we are able to predict the shapes of the post-synaptic firing rate histograms that may be tested on experimental data. Likewise, optimal stimulus input distributions are predicted, however, we show that the optimum can essentially be reached with a broad range of input distributions. Finally, we examine which parameters of the used neuronal model are the most important for the metabolically efficient information transfer.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
Project
<a href="/en/project/GA17-06943S" target="_blank" >GA17-06943S: Neural coding precision and its adaptation to the stimulus statistics</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PLoS Computational Biology
ISSN
1553-7358
e-ISSN
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Volume of the periodical
15
Issue of the periodical within the volume
12
Country of publishing house
US - UNITED STATES
Number of pages
21
Pages from-to
e1007545
UT code for WoS article
000507310800018
EID of the result in the Scopus database
2-s2.0-85076448863