ArticlePLoS computational biology2017
Linking structure and activity in nonlinear spiking networks.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 67 citations in OpenAlex.
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- Habit learning is associated with efficiently controlled network dynamics in naive macaque monkeys.Npj complexity · 2026Article
- Learning and inference with correlated neural variability.PNAS nexus · 2025Article
- Spiking dynamics of individual neurons reflect changes in the structure and function of neuronal networks.Nature communications · 2025Article
- Phase transitions inArXiv · 2025Article
- Temporal resolution of spike coding in feedforward networks with signal convergence and divergence.PLoS computational biology · 2025Article
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- Circuit-motivated generalized affine models characterize stimulus-dependent visual cortical shared variability.iScience · 2024Article
- Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes.Human brain mapping · 2023Article
- Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons.Communications biology · 2023Article
- Relating local connectivity and global dynamics in recurrent excitatory-inhibitory networks.PLoS computational biology · 2023Article
- Stochastic bursting in networks of excitable units with delayed coupling.Biological cybernetics · 2022Article
- Flexibility ofFrontiers in cellular neuroscience · 2022Article
- A data-informed mean-field approach to mapping of cortical parameter landscapes.PLoS computational biology · 2021Article
- Balanced networks under spike-time dependent plasticity.PLoS computational biology · 2021Article
- Nonequilibrium Green's Functions for Functional Connectivity in the Brain.Physical review letters · 2021Article
- Exploitation of Information as a Trading Characteristic: A Causality-Based Analysis of Simulated and Financial Data.Entropy (Basel, Switzerland) · 2020Article
- Before and beyond the Wilson-Cowan equations.Journal of neurophysiology · 2020Article
- Inferring and validating mechanistic models of neural microcircuits based on spike-train data.Nature communications · 2019Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.
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