Understanding neural coding on latent manifolds by sharing features and dividing ensembles

Klindt, David (October 2022) Understanding neural coding on latent manifolds by sharing features and dividing ensembles. arXiv. (Submitted)

DOI: 10.48550/arXiv.2210.03155


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.

Item Type: Paper
Subjects: bioinformatics
neurobiology > neuroscience
neurobiology > neuroscience > systems neuroscience
CSHL Authors:
Communities: CSHL labs > Klindt lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 6 October 2022
Date Deposited: 11 Apr 2024 15:59
Last Modified: 11 Apr 2024 15:59
Related URLs:
URI: https://repository.cshl.edu/id/eprint/41505

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