Original Research ARTICLE

Front. Comput. Neurosci., 28 October 2009 | doi: 10.3389/neuro.10.021.2009

Bayesian population decoding of spiking neurons

Computational Vision and Neuroscience Group, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
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.
Keywords:
Bayesian decoding, population coding, spiking neurons, approximate inference
Citation:
Gerwinn S, Macke J and Bethge M (2009). Bayesian population decoding of spiking neurons. Front. Comput. Neurosci. 3:21. doi: 10.3389/neuro.10.021.2009
Received:
23 April 2009;
 Paper pending published:
09 June 2009;
Accepted:
01 October 2009;
 Published online:
28 October 2009.

Edited by:

Wulfram Gerstner, Ecole Polytechnique Fédérale de Lausanne, Switzerland

Reviewed by:

Taro Toyoizumi, Columbia University, USA
Wulfram Gerstner, Ecole Polytechnique Fédérale de Lausanne, Switzerland
Copyright:
© 2009 Gerwinn, Macke and Bethge. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
*Correspondence:
Sebastian Gerwinn, Max Planck Institute for Biological Cybernetics, Computational Vision and Neuroscience Group, 72076 Tübingen, Germany. e-mail: sgerwinn@tuebingen.mpg.de
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