A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons

TL;DR

This paper introduces a biophysical model linking neural network activity to extracellular electric fields, improving the understanding of local field potentials in cortical pyramidal neurons.

Contribution

It develops a reduced three-compartment model for pyramidal neurons to derive a biophysically grounded observation model for local field potentials in neural networks.

Findings

The model aligns with the dipole assumption for cortical pyramidal cells.

Numerical simulations show improved accuracy over previous ad hoc models.

The approach facilitates comparison between neural activity and extracellular signals.

Abstract

We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced threecompartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential that contributes to the local field potential of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the dendritic field potential around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. [Mazzoni, A., S. Panzeri, N. K. Logothetis, and N. Brunel (2008). Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons. PLoS Computational Biology 4 (12), e1000239], and conclude that our biophysically motivated approach yields substantial improvement.