Functional architecture of M1 cells encoding movement direction

TL;DR

This paper develops a neurogeometrical model of M1 neurons encoding movement direction, incorporating fiber bundle structures, fragments, and spectral clustering to match experimental neural activity patterns.

Contribution

It introduces a novel geometric framework for modeling M1 neuron selectivity and movement fragments, extending previous models with a higher-dimensional structure and clustering analysis.

Findings

Model aligns well with experimental data

Spectral clustering recovers movement patterns

Provides a new geometric perspective on motor cortex activity

Abstract

In this paper we propose a neurogeometrical model of the behaviour of cells of the arm area of the primary motor cortex (M1). We will mathematically express as a fiber bundle the hypercolumnar organization of this cortical area, first modelled by Georgopoulos in \cite{georgopoulos1982relations, georgopoulos2015columnar}. On this structure, we will consider the selective tuning of M1 neurons of kinematic variables of positions and directions of movement. We will then extend this model to encode the notion of fragments introduced by Hatsopoulos \cite{Encoding} which describes the selectivity of neurons to movement direction varying in time. This leads to consider a higher dimensional geometrical structure where fragments are represented as integral curves. A comparison with the curves obtained through numerical simulations and experimental data will be presented. Moreover, neural activity shows coherent behaviours represented in terms of movement trajectories pointing to a specific pattern of movement decomposition \cite{kadmon2019movement}. Here, we will recover this pattern through a spectral clustering algorithm in the subriemannian structure we introduced, and compare our results with the neurophysiological one of \cite{kadmon2019movement}.