ArticleScientific reports2018
E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
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- Digital twins for understanding mechanisms of learning disabilities: Personalized deep neural networks reveal impact of neuronal hyperexcitability.bioRxiv : the preprint server for biology · 2024Article
- Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network.Frontiers in computational neuroscience · 2023Review
- Population and individual firing behaviors in sparsely synchronized rhythms in the hippocampal dentate gyrus.Cognitive neurodynamics · 2022Article
- Inhibitory control in neuronal networks relies on the extracellular matrix integrity.Cellular and molecular life sciences : CMLS · 2021Article
- Evolutionary conservation and divergence of the human brain transcriptome.Genome biology · 2021Article
- Emotions as Abstract Evaluation Criteria in Biological and Artificial Intelligences.Frontiers in computational neuroscience · 2021Article
- Local Homeostatic Regulation of the Spectral Radius of Echo-State Networks.Frontiers in computational neuroscience · 2021Article
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Authors and funding
3 authors.
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Abstract
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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