Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

TLDR

Leaky integrate‑and‑fire network models are widely used to study spiking dynamics, yet they cannot directly generate local field potentials because LFPs arise from spatially distributed membrane currents. The study aims to identify the best approximation for predicting LFPs from standard outputs of point‑neuron LIF networks. To do so, the authors compared candidate LFP proxies—such as firing rates, membrane potentials, and synaptic currents—to ground‑truth LFPs obtained by injecting the LIF network’s synaptic input currents into a realistic three‑dimensional multi‑compartmental neuron model with realistic morphology and spatial distributions. They found that a fixed linear combination of LIF synaptic currents accurately reproduces the LFP time course, explaining most of its variance across recording sites and remaining robust across diverse neuronal morphologies, thus providing a simple formula for estimating LFPs from LIF simulations when a single pyramidal population dominates.

Abstract

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best “LFP proxy”, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.