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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 420010.0, MPI für biologische Kybernetik / Biologische Kybernetik
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior
Authors:Gerwinn, S.; Macke, J.; Seeger, M.; Bethge, M.
Editors:Platt, J. C.; Koller, D.; Singer, Y.; Roweis, S.
Date of Publication (YYYY-MM-DD):2008-09
Title of Proceedings:Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference
Start Page:529
End Page:536
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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 estimates for the model parameters are essential.
We apply our method to multi-electrode recordings of retinal ganglion
cells and use our uncertainty 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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI f�r biologische Kybernetik/NWG Bethge
MPI f�r biologische Kybernetik/Empirical Inference (Dept. Sch�lkopf)
Identifiers:LOCALID:4728
URL:http://books.nips.cc/papers/files/nips20/NIPS2007_...
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