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

TL;DR

This paper introduces a hybrid modeling approach that combines simplified point-neuron network models with detailed biophysical neuron models to accurately predict local field potentials (LFPs) from neuronal networks.

Contribution

The authors develop a flexible hybrid scheme that separates network dynamics simulation from LFP generation, enabling efficient and detailed LFP predictions from point-neuron networks.

Findings

Successfully applied to a cortical network model of visual cortex

Predicted laminar LFPs for different network states

Analyzed the impact of synaptic correlations and neuron density on LFPs

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.