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Bayesian latent structure discovery from multi-neuron recordings
Machine Learning · Submitted 26 Oct 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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 P\'olya-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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Neural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s1 | However, traditional methods for dimensionality reduction and clustering are ill-suited to recovering the structure underlying the organization of neural circuits. | gap 0.91 | 0.06 | 0.03 | asserted 1.00 |
| s2 | 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. | gap 0.87 | 0.05 | 0.02 | asserted 1.00 |
| s3 | 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). | contribution 0.73 | 0.92 | 0.13 | asserted 0.99 |
| s4 | Our approach combines the GLM with flexible graph-theoretic priors governing the relationship between latent features and neural connectivity patterns. | method 0.98 | 0.83 | 0.06 | asserted 1.00 |
| s5 | Fully Bayesian inference via P\'olya-gamma augmentation of the resulting model allows us to classify neurons and infer latent dimensions of circuit organization from correlated spike trains. | method 0.99 | 0.89 | 0.20 | asserted 0.97 |
| s6 | 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. | result 0.90 | 0.97 | 0.72 | asserted 1.00 |
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Original abstract
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 P\'olya-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.
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Moreover, efficient algorithms are available to generate random variates [ref] .
Sampling Polya-Gamma random variates: alternate and approximate techniques · augmentation for discrete observations · 0.95
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