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  Neurometric function analysis of short-term population codes

Berens, P., Gerwinn, S., Ecker, A., & Bethge, M. (2009). Neurometric function analysis of short-term population codes. Frontiers in Computational Neuroscience, 2009(Conference Abstract: Bernstein Conference on Computational Neuroscience), 24-25. doi:10.3389/conf.neuro.10.2009.14.093.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-200B-D Version Permalink: http://hdl.handle.net/21.11116/0000-0003-200C-C
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Berens, P1, 2, Author              
Gerwinn, S1, 2, Author              
Ecker, AS1, 2, Author              
Bethge, M1, 2, Author              
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              


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 Abstract: The relative merits of different population coding schemes have mostly been studied in the framework of stimulus reconstruction using Fisher Information, minimum mean square error or mutual information. Here, we analyze neural population codes using the minimal discrimination error (MDE) and the Jensen-Shannon information in a two alternatives forced choice (2AFC) task. In a certain sense, this approach is more informative than the previous ones as it defines an error that is specific to any pair of possible stimuli - in particular, it includes Fisher Information as a special case. We demonstrate several advantages of the minimal discrimination error: (1) it is very intuitive and easier to compare to experimental data, (2) it is easier to compute than mutual information or minimum mean square error, (3) it allows studying assumption about prior distributions, and (4) it provides a more reliable assessment of coding accuracy than Fisher information. First, we introduce the Jensen-Shannon information and explain how it can be used to bound the MDE. In particular, we derive a new lower bound on the minimal discrimination error that is tighter than previous ones. Also, we explain how Fisher information can be derived from the Jensen-Shannon information and conversely to what extent Fisher information can be used to predict the minimal discrimination error for arbitrary pairs of stimuli depending on the properties of the tuning functions. Second, we use the minimal discrimination error to study population codes of angular variables. In particular, we assess the impact of different noise correlations structures on coding accuracy in long versus short decoding time windows. That is, for long time window we use the common Gaussian noise approximation while we analyze the Ising model with identical noise correlation structure to address the case of short time windows. As an important result, we find that the beneficial effect of stimulus dependent correlations in the absence of 'limited-range' correlations holds only true for long-term population codes while they provide no advantage in case of short decoding time windows. In this way, we provide for a new rigorous framework for assessing the functional consequences of correlation structures for the representational accuracy of neural population codes in short time scales.


 Dates: 2009-08
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.3389/conf.neuro.10.2009.14.093
 Degree: -


Title: Bernstein Conference on Computational Neuroscience (BCCN 2009)
Place of Event: Frankfurt a.M., Germany
Start-/End Date: 2009-09-30 - 2009-10-02

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Title: Frontiers in Computational Neuroscience
Source Genre: Journal
Publ. Info: Lausanne : Frontiers Research Foundation
Pages: - Volume / Issue: 2009 (Conference Abstract: Bernstein Conference on Computational Neuroscience) Sequence Number: - Start / End Page: 24 - 25 Identifier: Other: 1662-5188
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5188