Document Detail


Inferring decoding strategies from choice probabilities in the presence of correlated variability.
MedLine Citation:
PMID:  23313912     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
The activity of cortical neurons in sensory areas covaries with perceptual decisions, a relationship that is often quantified by choice probabilities. Although choice probabilities have been measured extensively, their interpretation has remained fraught with difficulty. We derive the mathematical relationship between choice probabilities, read-out weights and correlated variability in the standard neural decision-making model. Our solution allowed us to prove and generalize earlier observations on the basis of numerical simulations and to derive new predictions. Notably, our results indicate how the read-out weight profile, or decoding strategy, can be inferred from experimentally measurable quantities. Furthermore, we developed a test to decide whether the decoding weights of individual neurons are optimal for the task, even without knowing the underlying correlations. We confirmed the practicality of our approach using simulated data from a realistic population model. Thus, our findings provide a theoretical foundation for a growing body of experimental results on choice probabilities and correlations.
Authors:
Ralf M Haefner; Sebastian Gerwinn; Jakob H Macke; Matthias Bethge
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-13
Journal Detail:
Title:  Nature neuroscience     Volume:  -     ISSN:  1546-1726     ISO Abbreviation:  Nat. Neurosci.     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9809671     Medline TA:  Nat Neurosci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
1] Max Planck Institute for Biological Cybernetics, Tübingen, Germany. [2] Bernstein Center for Computational Neuroscience, Tübingen, Germany. [3] Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts, USA.
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