Bayesian latent structure discovery from multi-neuron recordings
This work addresses the challenge of analyzing heterogeneous neural circuits for neuroscientists, offering a novel method to uncover latent structure from noisy spike train data.
The authors tackled the problem of discovering latent structure in multi-neuron recordings, which traditional methods fail to handle due to noise and temporal dependencies, by developing a Bayesian hierarchical model that infers neural types and circuit organization from spike trains, demonstrating its effectiveness on synthetic data and primate retina recordings.
Neural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties. However, traditional methods for dimensionality reduction and clustering are ill-suited to recovering the structure underlying the organization of neural circuits. In particular, they do not take advantage of the rich temporal dependencies in multi-neuron recordings and fail to account for the noise in neural spike trains. Here we describe new tools for inferring latent structure from simultaneously recorded spike train data using a hierarchical extension of a multi-neuron point process model commonly known as the generalized linear model (GLM). Our approach combines the GLM with flexible graph-theoretic priors governing the relationship between latent features and neural connectivity patterns. Fully Bayesian inference via Pólya-gamma augmentation of the resulting model allows us to classify neurons and infer latent dimensions of circuit organization from correlated spike trains. We demonstrate the effectiveness of our method with applications to synthetic data and multi-neuron recordings in primate retina, revealing latent patterns of neural types and locations from spike trains alone.