title: Stimulus dependence of local field potential spectra: experiment versus theory.
abstract: The local field potential (LFP) captures different neural processes, including integrative synaptic dynamics that cannot be observed by measuring only the spiking activity of small populations. Therefore, investigating how LFP power is modulated by external stimuli can offer important insights into sensory neural representations. However, gaining such insight requires developing data-driven computational models that can identify and disambiguate the neural contributions to the LFP. Here, we investigated how networks of excitatory and inhibitory integrate-and-fire neurons responding to time-dependent inputs can be used to interpret sensory modulations of LFP spectra. We computed analytically from such models the LFP spectra and the information that they convey about input and used these analytical expressions to fit the model to LFPs recorded in V1 of anesthetized macaques (Macaca mulatta) during the presentation of color movies. Our expressions explain 60%-98% of the variance of the LFP spectrum shape and its dependency upon movie scenes and we achieved this with realistic values for the best-fit parameters. In particular, synaptic best-fit parameters were compatible with experimental measurements and the predictions of firing rates, based only on the fit of LFP data, correlated with the multiunit spike rate recorded from the same location. Moreover, the parameters characterizing the input to the network across different movie scenes correlated with cross-scene changes of several image features. Our findings suggest that analytical descriptions of spiking neuron networks may become a crucial tool for the interpretation of field recordings.
authors.affiliation: Institute for Scientific Interchange, 10133, Torino, Italy, Unit of Neuroscience Information and Complexity, Centre National de la Recherche Scientifique Unité Propre de Recherche-3293, 91198 Gif-sur-Yvette, France, barbieri@unic.cnrs-gif.fr.
authors.forename: Francesca
authors.initials: F
authors.lastname: Barbieri
authors.affiliation: Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068, Rovereto, Italy.
authors.forename: Alberto
authors.initials: A
authors.lastname: Mazzoni
authors.affiliation: Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany.
authors.forename: Nikos K
authors.initials: NK
authors.lastname: Logothetis
authors.affiliation: Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068, Rovereto, Italy, Institute of Neuroscience and Psychology, University of Glasgow, G12 8QB, Glasgow, United Kingdom, and.
authors.forename: Stefano
authors.initials: S
authors.lastname: Panzeri
authors.affiliation: Departments of Statistics and Neurobiology, The University of Chicago, Chicago, Illinois 60637.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Brunel
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D065818:Gamma Rhythm / Q000502:physiology*; D008251:Macaca; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D010775:Photic Stimulation; D014793:Visual Cortex / Q000502:physiology*
keywords: data-driven models; gamma oscillations; neural coding; primary visual cortex; recurrent networks; slow oscillations