Firing rate models for gamma oscillations
| Autor: | Áine Byrne, André A. Fenton, John Rinzel, Stephen Keeley |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Physics
Neurons Quantitative Biology::Neurons and Cognition Physiology General Neuroscience Models Neurological Brain Synaptic Potentials Dynamic feature 01 natural sciences Wilson–Cowan model 03 medical and health sciences 0302 clinical medicine Simple (abstract algebra) 0103 physical sciences Animals Gamma Rhythm Humans Statistical physics 010306 general physics 030217 neurology & neurosurgery Research Article |
| Zdroj: | J Neurophysiol |
| ISSN: | 1522-1598 |
| Popis: | Gamma oscillations are readily observed in a variety of brain regions during both waking and sleeping states. Computational models of gamma oscillations typically involve simulations of large networks of synaptically coupled spiking units. These networks can exhibit strongly synchronized gamma behavior, whereby neurons fire in near synchrony on every cycle, or weakly modulated gamma behavior, corresponding to stochastic, sparse firing of the individual units on each cycle of the population gamma rhythm. These spiking models offer valuable biophysical descriptions of gamma oscillations; however, because they involve many individual neuronal units they are limited in their ability to communicate general network-level dynamics. Here we demonstrate that few-variable firing rate models with established synaptic timescales can account for both strongly synchronized and weakly modulated gamma oscillations. These models go beyond the classical formulations of rate models by including at least two dynamic variables per population: firing rate and synaptic activation. The models’ flexibility to capture the broad range of gamma behavior depends directly on the timescales that represent recruitment of the excitatory and inhibitory firing rates. In particular, we find that weakly modulated gamma oscillations occur robustly when the recruitment timescale of inhibition is faster than that of excitation. We present our findings by using an extended Wilson-Cowan model and a rate model derived from a network of quadratic integrate-and-fire neurons. These biophysical rate models capture the range of weakly modulated and coherent gamma oscillations observed in spiking network models, while additionally allowing for greater tractability and systems analysis. NEW & NOTEWORTHY Here we develop simple and tractable models of gamma oscillations, a dynamic feature observed throughout much of the brain with significant correlates to behavior and cognitive performance in a variety of experimental contexts. Our models depend on only a few dynamic variables per population, but despite this they qualitatively capture features observed in previous biophysical models of gamma oscillations that involve many individual spiking units. |
| Databáze: | OpenAIRE |
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