title: Stimulus statistics shape oscillations in nonlinear 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 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.
authors.affiliation: Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology and Department of Clinical Neurosciences, University Hospital Center and University of Lausanne, 1011 Lausanne, Switzerland, jeremie.lefebvre@hotmail.com.
authors.forename: Jérémie
authors.initials: J
authors.lastname: Lefebvre
authors.affiliation: INRIA CR Nancy-Grand Est, Team NEUROSYS, Villers-les-Nancy, 54600, France.
authors.forename: Axel
authors.initials: A
authors.lastname: Hutt
authors.affiliation: Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology and Department of Clinical Neurosciences, University Hospital Center and University of Lausanne, 1011 Lausanne, Switzerland, EEG Brain Mapping Core, Centre for Biomedical Imaging (CIBM), 1011 Lausanne, Switzerland, and.
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Knebel
authors.affiliation: Department of Nuclear Medicine and Radiobiology, and Department of Diagnostic Radiology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, Québec, Canada, J1K 2R1.
authors.forename: Kevin
authors.initials: K
authors.lastname: Whittingstall
authors.affiliation: Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology and Department of Clinical Neurosciences, University Hospital Center and University of Lausanne, 1011 Lausanne, Switzerland, EEG Brain Mapping Core, Centre for Biomedical Imaging (CIBM), 1011 Lausanne, Switzerland, and.
authors.forename: Micah M
authors.identifier: http://orcid.org/0000-0002-7821-117X
authors.initials: MM
authors.lastname: Murray
mesh_terms: D000200:Action Potentials; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D010507:Periodicity
keywords: dynamics; networks; oscillations; spectrum; stimulation; synchrony