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Stimulus statistics shape oscillations in non-linear recurrent neural networks

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 amongst neural populations. Extending previous findings regarding stochastic non-linear 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 bi-directionally regulate both the frequency and power expressed by synchronous populations. These spectral fluctuations were further 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.
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Contributor : Axel Hutt Connect in order to contact the contributor
Submitted on : Saturday, December 6, 2014 - 7:36:09 PM
Last modification on : Tuesday, October 25, 2022 - 4:18:07 PM

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Jérémie Lefebvre, Axel Hutt, Jean-François Knebel, Kevin Whittingstall, Micah Murray. Stimulus statistics shape oscillations in non-linear recurrent neural networks. Journal of Neuroscience, 2015, 35 (7), pp.2895-2903. ⟨10.1523/JNEUROSCI.3609-14.2015⟩. ⟨hal-01091816⟩



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