Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles

TL;DR

This paper introduces a neural latent variable model that incorporates feature sharing and ensemble detection, improving interpretability and performance in modeling neural population activity on complex latent manifolds.

Contribution

It proposes feature sharing across tuning curves and an ensemble detection method via soft clustering, enhancing neural data modeling and interpretation.

Findings

Improved model performance on complex latent manifolds.

Successful unsupervised separation of neural ensembles.

Accurate inference of neural tuning and latent structures.

Abstract

Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives combine elegantly in neural latent variable models that constrain the relationship between latent variables and neural activity, modeled by simple tuning curve functions. This has recently been demonstrated using Gaussian processes, with applications to realistic and topologically relevant latent manifolds. Those and previous models, however, missed crucial shared coding properties of neural populations. We propose feature sharing across neural tuning curves which significantly improves performance and helps optimization. We also propose a solution to the ensemble detection problem, where different groups of neurons, i.e., ensembles, can be modulated by different latent manifolds. Achieved through a soft clustering of neurons during training, this allows for the separation of mixed neural populations in an unsupervised manner. These innovations lead to more interpretable models of neural population activity that train well and perform better even on mixtures of complex latent manifolds. Finally, we apply our method on a recently published grid cell dataset, and recover distinct ensembles, infer toroidal latents and predict neural tuning curves in a single integrated modeling framework.