Encoding of Naturalistic Stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons

By Alberto Mazzoni, Stefano Panzeri, Nikos K. Logothetis, Nicolas J.-B. Brunel. PLoS Comput. Biol., 2008

342 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e763dabbcf4d6a5aa455de4e7744f2be34a874b6

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Citations in the corpus

Neural population coding: combining insights from microscopic and mass signals (2015, background): “Experimental data and network models show that slow brain rhythms entrain to low-frequency variations of natural stimuli such as speech, directly inducing sensory information in the phase of these signals (e.g., [91,92,116,117]).”
The benefits of noise in neural systems: bridging theory and experiment (2011, methodology): “As a first example of the approach that we advocate, we consider the study of Mazzoni et al”
Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models (2015, methodology): “Likewise, the LFP proxies will be based only on input onto excitatory neurons (as done previously [15]).”
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition (2010, background): “It will be interesting to see, in future work, whether the observed phase relationships of firing activity and synaptic currents can be quantitatively accounted for by the theory of sparsely synchronized oscillations (124, 127, 342, 662).”
Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions. (2015, neutral): “While slow oscillations in sensory areas re ﬂ ect the rhythm of the sensory signal or of sensory sampling, fast gamma oscillations underpin ﬁ ne-grained sensory processing in a bottom-up fashion [28,39,74,83].”
A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings (2023, unknown): “We validated essential properties - which have been already extensively investigated elsewhere 34,37 - of the neural dynamics network with different expressions for 𝜈 𝑠𝑖𝑔𝑛𝑎𝑙 (𝑡) : sustained signals, periodically modulated signals and signals modulated with a Gaussian profile (Supplementary…”