Bayesian Inference for Spiking Neuron Models with a Sparsity Prior

Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full posterior distribution over all weights. In addition, we use a Laplacian prior to favor sparse solutions. Therefore, stimulus features that do not critically influence neural activity will be assigned zero weights and thus be effectively excluded by the model. This feature selection mechanism facilitates both the interpretation of the neuron model as well as its predictive abilities. The posterior distribution can be used to obtain confidence intervals which makes it possible to assess the statistical significance of the solution. In neural data analysis, the available amount of experimental measurements is often limited whereas the parameter space is large. In such a situation, both regularization by a sparsity prior and uncertainty es...

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Sebastian Gerwinn, Jakob Macke, Matthias Seeger, Matthias Bethge
• Keywords: Information Technology, NIPS 2007, Posterior Distribution, Statistical Significance, Uncertainty Estimate