Sebastian Gerwinn, Matthias Bethge, Jakob H. Macke, Matthias Seeger
Generalized linear models are the most commonly used tools to describe the stim- ulus 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 in- terpretation 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 pa- rameter space is large. In such a situation, both regularization by a sparsity prior and uncertainty estimates for the model parameters are essential. We apply our method to multi-electrode recordings of retinal ganglion cells and use our uncer- tainty estimate to test the statistical signiﬁcance of functional couplings between neurons. Furthermore we used the sparsity of the Laplace prior to select those ﬁlters from a spike-triggered covariance analysis that are most informative about the neural response.