# Bayesian Spike-Triggered Covariance Analysis

@inproceedings{Park2011BayesianSC, title={Bayesian Spike-Triggered Covariance Analysis}, author={Il Memming Park and Jonathan W. Pillow}, booktitle={NIPS}, year={2011} }

Neurons typically respond to a restricted number of stimulus features within the high-dimensional space of natural stimuli. Here we describe an explicit model-based interpretation of traditional estimators for a neuron's multi-dimensional feature space, which allows for several important generalizations and extensions. First, we show that traditional estimators based on the spike-triggered average (STA) and spike-triggered covariance (STC) can be formalized in terms of the "expected log…

## 64 Citations

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The generalized quadratic model provides a natural framework for combining multi-dimensional stimulus sensitivity and spike-history dependencies within a single model and provides closed-form estimators under a large class of non-Gaussian stimulus distributions.

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This work encodes prior knowledge for estimation of STRFs by choosing a set of basis function with the desired properties: a natural cubic spline basis, which is computationally efficient, and can be easily applied to Linear-Gaussian and Linear- nonlinear-Poisson models as well as more complicated Linear-Nonlinear-Linear-Non linear cascade model or spike-triggered clustering methods.

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