ArticlePLoS computational biology2017
Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.
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 57 papers.
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57 citing papers in PubMed.
- Neural population models for EEG: From Canonical models to alternative model structures.PLoS computational biology · 2026Article
- Simultaneous two- and three-photon multiplane imaging across cortical layers in freely moving mice.Nature methods · 2026Article
- Deterministic, stochastic, and mean-field PDE models in neuroscience.Frontiers in computational neuroscience · 2026Review
- Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Interleaving asynchronous and synchronous activity in balanced cortical networks with short term synaptic depression.Nature communications · 2025Article
- A refractory density approach to a multi-scale SEIRS epidemic model.Infectious Disease Modelling · 2025Article
- Understanding the Intersection Between Hormonal Dynamics and Brain Plasticity in Alzheimer's Disease: A Narrative Review for Implementing New Therapeutic Strategies.Health science reports · 2025Review
- Cell-type-specific firing patterns in a V1 cortical column model depend on feedforward and feedback-driven states.PLoS computational biology · 2025Article
- Article
- Efficient coding in biophysically realistic excitatory-inhibitory spiking networks.bioRxiv : the preprint server for biology · 2025Article
- Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Analyzing top-down visual attention in the context of gamma oscillations: a layer- dependent network-of- networks approach.Frontiers in computational neuroscience · 2024Article
- Metamodelling of a two-population spiking neural network.PLoS computational biology · 2023Article
- A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics.PLoS computational biology · 2023Article
- Targeting operational regimes of interest in recurrent neural networks.PLoS computational biology · 2023Article
- Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.PLoS computational biology · 2022Article
- Brain signal predictions from multi-scale networks using a linearized framework.PLoS computational biology · 2022Article
- A framework for macroscopic phase-resetting curves for generalised spiking neural networks.PLoS computational biology · 2022Article
- Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex.Nature communications · 2022Article
- A numerical population density technique for N-dimensional neuron models.Frontiers in neuroinformatics · 2022Article
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3 authors.
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Abstract
Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.
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