ArticlePLoS computational biology2020
Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Neuronal heterogeneity of normalization strength in a circuit model.Science advances · 2026Article
- Topological analysis of neuronal assemblies reveals low-rank structure modulated by cholinergic activity.bioRxiv : the preprint server for biology · 2025Article
- Balanced state of networks of winner-take-all units.PLoS computational biology · 2025Article
- Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex.eLife · 2025Article
- Excitatory/Inhibitory balance in autism spectrum disorders: Integrating genetic, neurotransmitter and computational perspectives.AIMS neuroscience · 2025Review
- Investigating the ability of astrocytes to drive neural network synchrony.PLoS computational biology · 2023Article
- Geometry of population activity in spiking networks with low-rank structure.PLoS computational biology · 2023Article
- Distributing task-related neural activity across a cortical network through task-independent connections.Nature communications · 2023Article
- Targeting operational regimes of interest in recurrent neural networks.PLoS computational biology · 2023Article
- Nonlinear transient amplification in recurrent neural networks with short-term plasticity.eLife · 2021Article
- What is the dynamical regime of cerebral cortex?Neuron · 2021Review
- Balanced networks under spike-time dependent plasticity.PLoS computational biology · 2021Article
- Training Spiking Neural Networks in the Strong Coupling Regime.Neural computation · 2021Article
- Emergence of Irregular Activity in Networks of Strongly Coupled Conductance-Based Neurons.Physical review. XArticle
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Authors and funding
3 authors.
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Abstract
Balanced excitation and inhibition is widely observed in cortex. How does this balance shape neural computations and stimulus representations? This question is often studied using computational models of neuronal networks in a dynamically balanced state. But balanced network models predict a linear relationship between stimuli and population responses. So how do cortical circuits implement nonlinear representations and computations? We show that every balanced network architecture admits stimuli that break the balanced state and these breaks in balance push the network into a "semi-balanced state" characterized by excess inhibition to some neurons, but an absence of excess excitation. The semi-balanced state produces nonlinear stimulus representations and nonlinear computations, is unavoidable in networks driven by multiple stimuli, is consistent with cortical recordings, and has a direct mathematical relationship to artificial neural networks.
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