Stimulus statistics shape oscillations in nonlinear recurrent neural networks.

Details

Ressource 1Download: 2895.full.pdf (1962.06 [Ko])
State: Public
Version: Final published version
Serval ID
serval:BIB_CAD7C0CE242C
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Stimulus statistics shape oscillations in nonlinear recurrent neural networks.
Journal
Journal of Neuroscience
Author(s)
Lefebvre J., Hutt A., Knebel J.F., Whittingstall K., Murray M.M.
ISSN
1529-2401 (Electronic)
ISSN-L
0270-6474
Publication state
Published
Issued date
2015
Peer-reviewed
Oui
Volume
35
Number
7
Pages
2895-2903
Language
english
Notes
Publication types: Journal Article Publication Status: ppublish
Abstract
Rhythmic activity plays a central role in neural computations and brain functions ranging from homeostasis to attention, as well as in neurological and neuropsychiatric disorders. Despite this pervasiveness, little is known about the mechanisms whereby the frequency and power of oscillatory activity are modulated, and how they reflect the inputs received by neurons. Numerous studies have reported input-dependent fluctuations in peak frequency and power (as well as couplings across these features). However, it remains unresolved what mediates these spectral shifts among neural populations. Extending previous findings regarding stochastic nonlinear systems and experimental observations, we provide analytical insights regarding oscillatory responses of neural populations to stimulation from either endogenous or exogenous origins. Using a deceptively simple yet sparse and randomly connected network of neurons, we show how spiking inputs can reliably modulate the peak frequency and power expressed by synchronous neural populations without any changes in circuitry. Our results reveal that a generic, non-nonlinear and input-induced mechanism can robustly mediate these spectral fluctuations, and thus provide a framework in which inputs to the neurons bidirectionally regulate both the frequency and power expressed by synchronous populations. Theoretical and computational analysis of the ensuing spectral fluctuations was found to reflect the underlying dynamics of the input stimuli driving the neurons. Our results provide insights regarding a generic mechanism supporting spectral transitions observed across cortical networks and spanning multiple frequency bands.
Pubmed
Web of science
Open Access
Yes
Create date
26/03/2015 19:28
Last modification date
20/08/2019 16:45
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