ArticleJournal of computational neuroscience2015
Emergent spike patterns in neuronal populations.
Article in Journal of computational neuroscience, 2015. 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.
- Article
- Firing rate models for gamma oscillations in I-I and E-I networks.Journal of computational neuroscience · 2024Article
- Multilevel monte carlo for cortical circuit models.Journal of computational neuroscience · 2022Article
- The Use of Reduced Models to Generate Irregular, Broad-Band Signals That Resemble Brain Rhythms.Frontiers in computational neuroscience · 2022Article
- A data-informed mean-field approach to mapping of cortical parameter landscapes.PLoS computational biology · 2021Article
- A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.PLoS computational biology · 2021Article
- Malleability of gamma rhythms enhances population-level correlations.Journal of computational neuroscience · 2021Article
- Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds.Frontiers in computational neuroscience · 2021Article
- Effective behavior of cooperative and nonidentical molecular motors.Research in the mathematical sciences · 2020Article
- DNN-assisted statistical analysis of a model of local cortical circuits.Scientific reports · 2020Article
- Stochastic neural field model: multiple firing events and correlations.Journal of mathematical biology · 2019Article
- How well do reduced models capture the dynamics in models of interacting neurons?Journal of mathematical biology · 2019Article
- Rhythm and Synchrony in a Cortical Network Model.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Article
- Orientation Selectivity from Very Sparse LGN Inputs in a Comprehensive Model of Macaque V1 Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2016Article
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2 authors.
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Abstract
This numerical study documents and analyzes emergent spiking behavior in local neuronal populations. Emphasis is given to a phenomenon we call clustering, by which we refer to a tendency of random groups of neurons large and small to spontaneously coordinate their spiking activity in some fashion. Using a sparsely connected network of integrate-and-fire neurons, we demonstrate that spike clustering occurs ubiquitously in both high firing and low firing regimes. As a practical tool for quantifying such spike patterns, we propose a simple scheme with two parameters, one setting the temporal scale and the other the amount of deviation from the mean to be regarded as significant. Viewing population activity as a sequence of events, meaning relatively brief durations of elevated spiking, separated by inter-event times, we observe that background activity tends to give rise to extremely broad distributions of event sizes and inter-event times, while driving a system imposes a certain regularity on its inter-event times, producing a rhythm consistent with broad-band gamma oscillations. We note also that event sizes and inter-event times decorrelate very quickly. Dynamical analyses supported by numerical evidence are offered.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.