English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification

Theis, L., Chagas, A., Arnstein, D., Schwarz, C., & Bethge, M. (2013). Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification. PLoS Computational Biology, 9(11), 1-9. doi:10.1371/journal.pcbi.1003356.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-001A-127D-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-859C-9
Genre: Journal Article

Files

show Files

Creators

show
hide
 Creators:
Theis, L, Author              
Chagas , AM, Author
Arnstein, Dan, Author              
Schwarz, C, Author
Bethge, M1, 2, Author              
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: Generalized linear models (GLMs) represent a popular choice for the probabilistic characterization of neural spike responses. While GLMs are attractive for their computational tractability, they also impose strong assumptions and thus only allow for a limited range of stimulus-response relationships to be discovered. Alternative approaches exist that make only very weak assumptions but scale poorly to high-dimensional stimulus spaces. Here we seek an approach which can gracefully interpolate between the two extremes. We extend two frequently used special cases of the GLM—a linear and a quadratic model—by assuming that the spike-triggered and non-spike-triggered distributions can be adequately represented using Gaussian mixtures. Because we derive the model from a generative perspective, its components are easy to interpret as they correspond to, for example, the spike-triggered distribution and the interspike interval distribution. The model is able to capture complex dependencies on high-dimensional stimuli with far fewer parameters than other approaches such as histogram-based methods. The added flexibility comes at the cost of a non-concave log-likelihood. We show that in practice this does not have to be an issue and the mixture-based model is able to outperform generalized linear and quadratic models.

Details

show
hide
Language(s):
 Dates: 2013-11
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1371/journal.pcbi.1003356
eDoc: e1003356
BibTex Citekey: TheisCASB2013
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: PLoS Computational Biology
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 9 (11) Sequence Number: - Start / End Page: 1 - 9 Identifier: -