Hybrid scheme for modeling local field potentials from point-neuron networks

Abstract

Due to rapid advances in multielectrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both basic research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining the efficiency of commonly used simplified point-neuron network models with the biophysical principles underlying LFP generation by real neurons. The scheme can be used with an arbitrary number of point-neuron network populations. The LFP predictions rely on populations of network-equivalent, anatomically reconstructed multicompartment neuron models with layer-specific synaptic connectivity. The present scheme allows for a full separation of the network dynamics simulation and LFP generation. For illustration, we apply the scheme to a full-scale cortical network model for a $\sim$1 mm${}^2$ patch of primary visual cortex and predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate the role of synaptic input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its publicly available implementation in \texttt{hybridLFPy} form the basis for LFP predictions from other point-neuron network models, as well as extensions of the current application to larger circuitry and additional biological detail.

References in corpus (16)

• The structure and function of complex networks
• Surrogate time series
• Macroscopic models of local field potentials and the apparent 1/f noise in brain activity
• Decorrelation of neural-network activity by inhibitory feedback
• A constructive mean field analysis of multi population neural networks with random synaptic weights and stochastic inputs
• Frequency dependence of signal power and spatial reach of the local field potential
• Full-density multi-scale account of structure and dynamics of macaque visual cortex
• The Local Field Potential Reflects Surplus Spike Synchrony
• Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations
• Active subthreshold dendritic conductances shape the local field potential
• Identifying anatomical origins of coexisting oscillations in the cortical microcircuit
• Echoes in correlated neural systems
• Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): Comparing multi-electrode recordings from simulated and biological mammalian cortical tissue
• Modulated escape from a metastable state driven by colored noise
• Statistical physics of neural systems with non-additive dendritic coupling
• A Generic Approach to Solving Jump Diffusion Equations with Applications to Neural Populations

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