Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons

Nikos Logothetis, Alberto Mazzoni, Stefano Panzeri, Nikos K. Logothetis, Nicolas Brunel

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    Recordings of local field potentials (LFPs) reveal that the sensory cortex displays rhythmic activity and fluctuations over a wide range of frequencies and amplitudes. Yet, the role of this kind of activity in encoding sensory information remains largely unknown. To understand the rules of translation between the structure of sensory stimuli and the fluctuations of cortical responses, we simulated a sparsely connected network of excitatory and inhibitory neurons modeling a local cortical population, and we determined how the LFPs generated by the network encode information about input stimuli. We first considered simple static and periodic stimuli and then naturalistic input stimuli based on electrophysiological recordings from the thalamus of anesthetized monkeys watching natural movie scenes. We found that the simulated network produced stimulus-related LFP changes that were in striking agreement with the LFPs obtained from the primary visual cortex. Moreover, our results demonstrate that the network encoded static input spike rates into gamma-range oscillations generated by inhibitory-excitatory neural interactions and encoded slow dynamic features of the input into slow LFP fluctuations mediated by stimulus-neural interactions. The model cortical network processed dynamic stimuli with naturalistic temporal structure by using low and high response frequencies as independent communication channels, again in agreement with recent reports from visual cortex responses to naturalistic movies. One potential function of this frequency decomposition into independent information channels operated by the cortical network may be that of enhancing the capacity of the cortical column to encode our complex sensory environment. © 2008 Mazzoni et al.
    Original languageEnglish
    Article numbere1000239
    JournalPL o S Computational Biology
    Issue number12
    Publication statusPublished - Dec 2008


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