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Input dependence of local field potential spectra: experiment vs theory

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Logothetis,  Nikos K
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Barbieri, F., Mazzoni, A., Logothetis, N. K., Panzeri, S., & Brunel, N. (2013). Input dependence of local field potential spectra: experiment vs theory. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2013), Salt Lake City, UT, USA.


Cite as: http://hdl.handle.net/21.11116/0000-0001-1891-0
Abstract
How sensory stimuli are encoded in neuronal activity is a major challenge for understanding perception. A prominent effect of sensory stimulation is to elicit oscillations in EEG and Local Field Potential (LFP) recordings over a broad range of frequencies. Belitski et al. recorded LFPs and spiking activity in the primary visual cortex of anaesthetized macaques presented with naturalistic movies and found that the power of the gamma and low-frequency bands of LFP carried largely independent information about visual stimuli, while the information carried by the spiking activity was largely redundant with that carried by the gamma-band LFPs. To understand better how different frequency bands of the LFP are controlled by sensory input, we computed analytically the power spectrum of the LFP of a theoretical model of V1 (a network composed of two populations of neurons - excitatory and inhibitory), subjected to time-dependent external inputs modelling inputs from the LGN, as a function of the parameters characterizing single neurons, synaptic connectivity, as well as parameters characterizing the statistics of external inputs. We then devised an algorithm to fit the data using these analytical results. The data consists in LFP recordings in the visual cortex of macaques, during presentation of a naturalistic movie. This fitting procedure permits to extract the temporal evolution, during the movie presentation, of both the network parameters, such as the excitatory and inhibitory firing rates, and the parameters of the input, such as for example its typical time scales. We found that the average firing rates extracted from the fits correlates significantly with the multi-unit activity. Furthermore we found a significant correlation between the parameters that describe the input and the features of the movie, such as for example the temporal contrast.