4497745
doi
10.5281/zenodo.4497745
oai:zenodo.org:4497745
user-in-fet
user-eu
Renaud B. Jolivet
Department of Nuclear and Corpuscular PhysicsUniversity of GenevaGenevaSwitzerland
Modelling Neuromodulated Information Flow and Energetic Consumption at Thalamic Relay Synapses
Mireille CONRAD
Department of Nuclear and Corpuscular PhysicsUniversity of GenevaGenevaSwitzerland
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Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>Abstract. Recent experimental and theoretical work has shown that synapses in the visual pathway balance information flow with their en- ergetic needs, maximising not the information flow from the retina to the primary visual cortex (bits per second), but instead maximising in- formation flow per concomitant energy consumption (bits of information transferred per number of adenosine triphosphate molecules necessary to power the corresponding synaptic and neuronal activities)[10,5,11]. We have previously developed a biophysical Hodgkin-Huxley-type model for thalamic relay cells, calibrated on experimental data, and that re- capitulates those experimental findings[10]. Here, we introduce an im- proved version of that model to include neuromodulation of thalamic relay synapses’ transmission properties by serotonin. We show how sig- nificantly neuromodulation affects the output of thalamic relay cells, and discuss the implications of that mechanism in the context of energetically optimal information transfer at those synapses.</p>
Zenodo
2020-10-01
info:eu-repo/semantics/article
4497744
user-in-fet
user-eu
Accepted version
award_title=Ionic Neuromodulation For Epilepsy Treatment; award_number=862882; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/862882; funder_id=00k4n6c32; funder_name=European Commission;
1618489639.813276
726076
md5:7f9d3ffcaabec1b359446dbe0d419cbc
https://zenodo.org/records/4497745/files/2020_ICANN_2020_Conrad_R1.pdf
public
10.5281/zenodo.4497744
isVersionOf
doi
Springer International Publishing
Vol. 12397 LNCS
Artificial Neural Networks and Machine Learning – ICANN 2020
649-658
2020-10-01