Toolbox for inference in generalized linear models of spiking neurons
-
1
Max Planck Institute for Biological Cybernetics, NWG Bethge, Germany
-
2
University of Tübingen, Center for Integrative Neuroscience and Institute for Theoretical Physics, Germany
-
3
University College London, Gatsby Computational Neuroscience Unit, United Kingdom
Generalized linear models are increasingly used for analyzing neural data, and to characterize the stimulus dependence and functional connectivity of both single neurons and neural populations. One possibility to extend the computational complexity of these models is to expand the stimulus, and possibly the representation of the spiking history into high dimensional feature spaces.
When the dimension of the parameter space is large, strong regularization has to be used in order to fit GLMs to datasets of realistic size without overfitting. By imposing properly chosen priors over parameters, Bayesian inference provides an effective and principled approach for achieving regularization.
In this work, we present a MATLAB toolbox which provides efficient inference methods for parameter fitting. This includes standard maximum a posteriori estimation for Gaussian and Laplacian prior, which is also sometimes referred to as L1- and L2-reguralization. Furthermore, it implements approximate inference techniques for both prior distributions based on the expectation propagation algorithm [1].
In order to model the refractory property and functional couplings between neurons, the spiking history within a population is often represented as responses to a set of predefined basis functions. Most of the basis function sets used so far, are non-orthogonal. Commonly priors are specified without taking the properties of the basis functions into account (uncorrelated Gauss, independent Laplace). However, if basis functions overlap, the coefficients are correlated. As an example application of this toolbox, we analyze the effect of independent prior distributions, if the set of basis functions are non-orthogonal and compare the performance to the orthogonal setting.
References
[1] Gerwinn S, Macke JH and Bethge M (2010) Bayesian inference for generalized linear models for spiking neurons. Front. Comput. Neurosci. 4:12. doi:10.3389/fncom.2010.00012
Keywords:
computational neuroscience
Conference:
Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010.
Presentation Type:
Presentation
Topic:
Bernstein Conference on Computational Neuroscience
Citation:
Gerwinn
S,
Macke
JH and
Bethge
M
(2010). Toolbox for inference in generalized linear models of spiking neurons.
Front. Comput. Neurosci.
Conference Abstract:
Bernstein Conference on Computational Neuroscience.
doi: 10.3389/conf.fncom.2010.51.00091
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
13 Sep 2010;
Published Online:
23 Sep 2010.
*
Correspondence:
Dr. Sebastian Gerwinn, Max Planck Institute for Biological Cybernetics, NWG Bethge, Tübingen, Germany, sebastian.gerwinn@tuebingen.mpg.de