ArticlePLoS computational biology2020
Response nonlinearities in networks of spiking neurons.
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 19 papers.
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Who cites it
19 citing papers in PubMed.
- Visual learning at fast and slow timescales is driven by distinct plasticity rules in primate inferotemporal cortex.Nature communications · 2026Article
- Voltage imaging as a window into neural computation.Neurophotonics · 2026Article
- A theory for self-sustained balanced states in absence of strong external currents.PLoS computational biology · 2026Article
- Cooperative coding of continuous variables in networks with sparsity constraint.PLoS computational biology · 2025Article
- Balanced state of networks of winner-take-all units.PLoS computational biology · 2025Article
- Stabilized Supralinear Network Model of Responses to Surround Stimuli in Primary Visual Cortex.eNeuro · 2025Article
- Prefrontal working memory signal controls phase-coded information within extrastriate cortex.bioRxiv : the preprint server for biology · 2025Article
- Fast nonlinear integration drives accurate encoding of input information in large multiscale systems.Communications physics · 2025Article
- Cellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Single-cell optogenetics reveals attenuation-by-suppression in visual cortical neurons.bioRxiv : the preprint server for biology · 2024Article
- Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys.Neuron · 2023Article
- Targeting operational regimes of interest in recurrent neural networks.PLoS computational biology · 2023Article
- Information representation in an oscillating neural field model modulated by working memory signals.Frontiers in computational neuroscience · 2023Article
- Sparse balance: Excitatory-inhibitory networks with small bias currents and broadly distributed synaptic weights.PLoS computational biology · 2022Article
- NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models.Frontiers in neuroinformatics · 2022Article
- A network model of the barrel cortex combined with a differentiator detector reproduces features of the behavioral response to single-neuron stimulation.PLoS computational biology · 2021Article
- Single-neuron dynamical effects of dendritic pruning implicated in aging and neurodegeneration: towards a measure of neuronal reserve.Scientific reports · 2021Article
- Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance.PLoS computational biology · 2020Article
- Emergence of Irregular Activity in Networks of Strongly Coupled Conductance-Based Neurons.Physical review. XArticle
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3 authors.
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Abstract
Combining information from multiple sources is a fundamental operation performed by networks of neurons in the brain, whose general principles are still largely unknown. Experimental evidence suggests that combination of inputs in cortex relies on nonlinear summation. Such nonlinearities are thought to be fundamental to perform complex computations. However, these non-linearities are inconsistent with the balanced-state model, one of the most popular models of cortical dynamics, which predicts networks have a linear response. This linearity is obtained in the limit of very large recurrent coupling strength. We investigate the stationary response of networks of spiking neurons as a function of coupling strength. We show that, while a linear transfer function emerges at strong coupling, nonlinearities are prominent at finite coupling, both at response onset and close to saturation. We derive a general framework to classify nonlinear responses in these networks and discuss which of them can be captured by rate models. This framework could help to understand the diversity of non-linearities observed in cortical networks.
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