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Bayesian Inference for Spiking Neuron Models with a Sparsity Prior

2008

Conference Paper

ei


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.

Author(s): Gerwinn, S. and Macke, J. and Seeger, M. and Bethge, M.
Book Title: Advances in neural information processing systems 20
Journal: Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007
Pages: 529-536
Year: 2008
Month: September
Day: 0
Editors: Platt, J. C., D. Koller, Y. Singer, S. Roweis
Publisher: Curran

Department(s): Empirische Inferenz
Bibtex Type: Conference Paper (inproceedings)

Event Name: Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007)
Event Place: Vancouver, BC, Canada

Address: Red Hook, NY, USA
Digital: 0
ISBN: 978-1-605-60352-0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{4728,
  title = {Bayesian Inference for Spiking Neuron Models with a Sparsity Prior},
  author = {Gerwinn, S. and Macke, J. and Seeger, M. and Bethge, M.},
  journal = {Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007},
  booktitle = {Advances in neural information processing systems 20},
  pages = {529-536},
  editors = {Platt, J. C., D. Koller, Y. Singer, S. Roweis},
  publisher = {Curran},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Red Hook, NY, USA},
  month = sep,
  year = {2008},
  month_numeric = {9}
}