Lattice Field Theory for a network of real neurons

TL;DR

This paper introduces a Lattice Field Theory framework that interprets BCI neural recordings through a physically grounded, time-evolving maximum entropy model aligned with the Free Energy principle.

Contribution

It extends the maximum entropy model to include temporal dynamics, providing a novel interpretation method for neural data from BCIs.

Findings

Framework effectively interprets spike rasters from single neuron activity.

Incorporates time evolution into maximum entropy models for neural networks.

Aligns with the Free Energy principle for neural system analysis.

Abstract

In a recent paper [Bardella et al., Entropy 26 (6), 495 (2024)] we introduced a simplified Lattice Field Theory (LFT) framework that allows experimental recordings from major Brain-Computer Interfaces (BCIs) to be interpreted in a simple and physically grounded way. From a neuroscience point of view, our method modifies the Maximum Entropy model for neural networks so that also the time evolution of the system is taken into account and it can be interpreted as another version of the Free Energy principle (FEP). This framework is naturally tailored to interpret recordings from chronic multi-site BCIs, especially spike rasters from measurements of single neuron activity.