ArticlePLoS computational biology2017
Firing rate equations require a spike synchrony mechanism to correctly describe fast oscillations in inhibitory 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 29 papers.
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29 citing papers in PubMed, 92 citations in OpenAlex.
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- How synaptic function controls critical transitions in spiking neuron networks: insight from a Kuramoto model reduction.Frontiers in network physiology · 2024Article
- Analyzing top-down visual attention in the context of gamma oscillations: a layer- dependent network-of- networks approach.Frontiers in computational neuroscience · 2024Article
- Periodic solutions in next generation neural field models.Biological cybernetics · 2023Article
- The inhibitory control of traveling waves in cortical networks.PLoS computational biology · 2023Article
- The impact of regional heterogeneity in whole-brain dynamics in the presence of oscillations.Network neuroscience (Cambridge, Mass.) · 2023Article
- Sex differences in amygdalohippocampal oscillations and neuronal activation in a rodent anxiety model and in response to infralimbic deep brain stimulation.Frontiers in behavioral neuroscience · 2023Article
- A mechanistic account of visual discomfort.Frontiers in neuroscience · 2023Article
- Interplay of different synchronization modes and synaptic plasticity in a system of class I neurons.Scientific reports · 2022Article
- Cross-scale excitability in networks of quadratic integrate-and-fire neurons.PLoS computational biology · 2022Article
- Optimal closed-loop deep brain stimulation using multiple independently controlled contacts.PLoS computational biology · 2021Article
- Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation.Frontiers in systems neuroscience · 2021Article
- Dynamics of a Large-Scale Spiking Neural Network with Quadratic Integrate-and-Fire Neurons.Neural plasticity · 2021Article
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3 authors at 2 institutions in 2 countries.
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Abstract
Recurrently coupled networks of inhibitory neurons robustly generate oscillations in the gamma band. Nonetheless, the corresponding Wilson-Cowan type firing rate equation for such an inhibitory population does not generate such oscillations without an explicit time delay. We show that this discrepancy is due to a voltage-dependent spike-synchronization mechanism inherent in networks of spiking neurons which is not captured by standard firing rate equations. Here we investigate an exact low-dimensional description for a network of heterogeneous canonical Class 1 inhibitory neurons which includes the sub-threshold dynamics crucial for generating synchronous states. In the limit of slow synaptic kinetics the spike-synchrony mechanism is suppressed and the standard Wilson-Cowan equations are formally recovered as long as external inputs are also slow. However, even in this limit synchronous spiking can be elicited by inputs which fluctuate on a time-scale of the membrane time-constant of the neurons. Our meanfield equations therefore represent an extension of the standard Wilson-Cowan equations in which spike synchrony is also correctly described.
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