State and Parameter Estimation for a Neural Model of Local Field Potentials

TL;DR

This paper presents a Bayesian data assimilation method to jointly estimate neural activity states and parameters in a Wilson--Cowan model from local field potential data, aiding understanding of cortical dynamics during sleep.

Contribution

It introduces a novel approach combining a neural field model with data assimilation for joint state and parameter estimation from LFP recordings.

Findings

Feasibility demonstrated on synthetic data

Applied successfully to real cortical LFP data

Potential to infer cortical stimulus and neural states

Abstract

The study of cortical dynamics during different states such as decision making, sleep and movement, is an important topic in Neuroscience. Modelling efforts aim to relate the neural rhythms present in cortical recordings to the underlying dynamics responsible for their emergence. We present an effort to characterize the neural activity from the cortex of a mouse during natural sleep, captured through local field potential measurements. Our approach relies on using a discretized Wilson--Cowan Amari neural field model for neural activity, along with a data assimilation method that allows the Bayesian joint estimation of the state and parameters. We demonstrate the feasibility of our approach on synthetic measurements before applying it to a dataset available in literature. Our findings suggest the potential of our approach to characterize the stimulus received by the cortex from other brain regions, while simultaneously inferring a state that aligns with the observed signal.