English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Bayesian population decoding of spiking neurons

Gerwinn, S., Macke, J., & Bethge, M. (2009). Bayesian population decoding of spiking neurons. Frontiers in Computational Neuroscience, 3: 21, pp. 1-28. doi:10.3389/neuro.10.021.2009.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C246-C Version Permalink: http://hdl.handle.net/21.11116/0000-0002-BB3E-7
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Gerwinn, S1, 2, Author              
Macke, JH1, 2, 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: The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a `spike-by-spike‘ online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

Details

show
hide
Language(s):
 Dates: 2009-10
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.3389/neuro.10.021.2009
BibTex Citekey: 6102
 Degree: -

Event

show

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: 3 Sequence Number: 21 Start / End Page: 1 - 28 Identifier: Other: 1662-5188
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5188