ArticlePLoS computational biology2022
Sparse balance: Excitatory-inhibitory networks with small bias currents and broadly distributed synaptic weights.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Dynamic Global and Local Enhancement of Theta-Band Functional Networks as Neural Markers of Spinal Cord Stimulation Efficacy in Disorders of Consciousness.CNS neuroscience & therapeutics · 2026Article
- Article
- A theory for self-sustained balanced states in absence of strong external currents.PLoS computational biology · 2026Article
- Balanced state of networks of winner-take-all units.PLoS computational biology · 2025Article
- Chronic modulation of cAMP signaling elicits synaptic scaling irrespective of activity.iScience · 2024Article
- Electric Field Effects on Brain Activity: Implications for Epilepsy and Burst Suppression.Cells · 2023Article
- A universal description of stochastic oscillators.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Targeting operational regimes of interest in recurrent neural networks.PLoS computational biology · 2023Article
- 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
- Input correlations impede suppression of chaos and learning in balanced firing-rate networks.PLoS computational biology · 2022Article
- What is the dynamical regime of cerebral cortex?Neuron · 2021Review
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Authors and funding
3 authors.
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Abstract
Cortical circuits generate excitatory currents that must be cancelled by strong inhibition to assure stability. The resulting excitatory-inhibitory (E-I) balance can generate spontaneous irregular activity but, in standard balanced E-I models, this requires that an extremely strong feedforward bias current be included along with the recurrent excitation and inhibition. The absence of experimental evidence for such large bias currents inspired us to examine an alternative regime that exhibits asynchronous activity without requiring unrealistically large feedforward input. In these networks, irregular spontaneous activity is supported by a continually changing sparse set of neurons. To support this activity, synaptic strengths must be drawn from high-variance distributions. Unlike standard balanced networks, these sparse balance networks exhibit robust nonlinear responses to uniform inputs and non-Gaussian input statistics. Interestingly, the speed, not the size, of synaptic fluctuations dictates the degree of sparsity in the model. In addition to simulations, we provide a mean-field analysis to illustrate the properties of these networks.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.