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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 461655.0, MPI für biologische Kybernetik / Biologische Kybernetik
Bayesian population decoding of spiking neurons
Authors:Gerwinn, S.; Macke, J.H.; Bethge, M.
Date of Publication (YYYY-MM-DD):2009-10
Title of Journal:Frontiers in Computational Neuroscience
Volume:3
Issue / Number:21
Start Page:1
End Page:28
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Article
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/NWG Bethge
Identifiers:LOCALID:6102
URL:http://www.frontiersin.org/computationalneuroscien...
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