pyglm | Interpretable neural spike train models | Machine Learning library

pyglm is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. pyglm has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

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. This repository contains 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). In the statistics and time series analysis communities, these correspond to nonlinear vector autoregressive processes with count observations. We combine the GLM with flexible graph-theoretic priors governing the relationship between latent features and neural connectivity patterns. Fully Bayesian inference via \polyagamma 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.