Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing EnsemblesDownload PDF

Published: 01 Feb 2023, Last Modified: 15 Feb 2023ICLR 2023 posterReaders: Everyone
Keywords: neuroscience, neural activity, tuning curves, neural ensemble detection, grid cells, latent variable models
TL;DR: We propose neural latent variable models with feature sharing and ensemble detection.
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 $\textit{feature sharing}$ across neural tuning curves which significantly improves performance and helps optimization. We also propose a solution to the $\textit{ensemble detection}$ 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.
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