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., Arnstein, D., Chagas, A., Schwarz, C., & Bethge, M. (2012). Beyond GLMs: a generative mixture modeling approach to neural system identification. Poster presented at Bernstein Conference 2012, München, Germany. doi:10.3389/conf.fncom.2012.55.00080.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B63E-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-9BEA-9
Genre: Poster

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Theis, LM, Author              
Arnstein, D, Author              
Chagas, AM, 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, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: One of the principle goals of sensory systems neuroscience is to characterize the relationship between external stimuli and neuronal responses. A popular choice for modeling the responses of neurons is the generalized linear model (GLM). However, due to its inherent linearity, choosing a set of nonlinear features is often crucial but can be difficult in practice if the stimulus dimensionality is high or if the stimulus-response dependencies are complex. Here, we derive a more flexible neuron model which is able to automatically extract highly nonlinear stimulus-response relationships from the data. We start out by representing intuitive and well understood distributions such as the spike-triggered and inter-spike interval distributions using nonparametric models. For instance, we use mixtures of Gaussians to represent spike-triggered distributions which allows for complex stimulus dependencies such as those of cells with multiple preferred stimuli. A simple application of Bayes’ rule allows us to turn these distributions into a model of the neuron’s response, which we dub spike-triggered mixture model (STM). We demonstrate the superior representational power of the STM by fitting it to data generated by a trained GLM and vice versa. While the STM is able to reproduce the behavior of the GLM, the opposite is not the case. We also apply our model to single-cell recordings of primary afferents of the rat’s whisker system and find quantitatively and qualitatively that it is able to better reproduce the cells’ behavior than the GLM. In particular, we obtain much higher estimates of the cells’ mutual information rates.

Details

show
hide
Language(s):
 Dates: 2012-09
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.3389/conf.fncom.2012.55.00080
BibTex Citekey: TheisACSB2012
 Degree: -

Event

show
hide
Title: Bernstein Conference 2012
Place of Event: München, Germany
Start-/End Date: -

Legal Case

show

Project information

show

Source 1

show
hide
Title: Frontiers in Computational Neuroscience
  Abbreviation : Front Comput Neurosci
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Lausanne : Frontiers Research Foundation
Pages: - Volume / Issue: 2012 (Conference Abstract: Bernstein Conference 2012) Sequence Number: - Start / End Page: 165 Identifier: Other: 1662-5188
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5188