E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks

  • Spontaneous brain activity is characterized in part by a balanced asynchronous chaotic state. Cortical recordings show that excitatory (E) and inhibitory (I) drivings in the E-I balanced state are substantially larger than the overall input. We show that such a state arises naturally in fully adapting networks which are deterministic, autonomously active and not subject to stochastic external or internal drivings. Temporary imbalances between excitatory and inhibitory inputs lead to large but short-lived activity bursts that stabilize irregular dynamics. We simulate autonomous networks of rate-encoding neurons for which all synaptic weights are plastic and subject to a Hebbian plasticity rule, the flux rule, that can be derived from the stationarity principle of statistical learning. Moreover, the average firing rate is regulated individually via a standard homeostatic adaption of the bias of each neuron’s input-output non-linear function. Additionally, networks with and without short-term plasticity are considered. E-I balance may arise only when the mean excitatory and inhibitory weights are themselves balanced, modulo the overall activity level. We show that synaptic weight balance, which has been considered hitherto as given, naturally arises in autonomous neural networks when the here considered self-limiting Hebbian synaptic plasticity rule is continuously active.

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Author:Philip Trapp, Rodrigo Echeveste, Claudius GrosORCiDGND
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/29895972
Parent Title (English):Scientific reports
Publisher:Macmillan Publishers Limited, part of Springer Nature
Place of publication:[London]
Document Type:Article
Year of Completion:2018
Date of first Publication:2018/06/12
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/06/19
Tag:Complex networks; Network models
Issue:1, Art. 8939
Page Number:12
First Page:1
Last Page:12
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Institutes:Physik / Physik
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Licence (German):License LogoCreative Commons - Namensnennung 4.0