Email updates

Keep up to date with the latest news and content from BMC Neuroscience and BioMed Central.

This article is part of the supplement: Twenty First Annual Computational Neuroscience Meeting: CNS*2012

Open Access Poster presentation

Optimal information encoding for multiple, simultaneously presented stimuli

Jan Pieczkowski12*, Lawrence York2, Jeanette Hellgren Kotaleski13 and Mark van Rossum2

Author Affiliations

1 Department of Computational Biology, CSC, Royal Institute of Technology, Stockholm, Sweden

2 Department of Informatics, Edinburgh University, Edinburgh, EH8 9AB, UK

3 Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden

For all author emails, please log on.

BMC Neuroscience 2012, 13(Suppl 1):P17  doi:10.1186/1471-2202-13-S1-P17

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/13/S1/P17


Published:16 July 2012

© 2012 Pieczkowski et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

Information in the brain is usually encoded in a way that distributes the activity over a population of neurons, referred to as population coding[1]. Population coding has been observed in almost all brain systems and renders the neural code robust, accurate, and failure resistant.

The coding of single stimuli in population codes is relatively well understood [2], and in particular the noise models, correlations, neural heterogeneity and links to psychophysics have been studied. However, the situation is much less clear when multiple stimuli are simultaneously encoded [3].

Theoretical studies (e.g., [4]) have thus far only examined linear supposition schemes that encode a probabilistic stimulus ensemble. However, experimental studies (c.f. [5], [6], [7]) suggest a non-linear encoding scheme using a maximum rule, where the response of a single neuron to a pair of stimuli equals the response to the constituent that on its own produces the maximum response, i.e.

We investigate the theoretical implications of these findings by comparing different encoding strategies and examine the decoding accuracy. The goal is to find the optimal encoding scheme for multiple stimuli.

We investigate the theoretical implications of these findings by comparing different encoding strategies and examine the decoding accuracy. The goal is to find the optimal encoding scheme for multiple stimuli.

In our current study, we focus on the simultaneous coding of visual stimuli representing overlapping movements of two groups of points in different directions. We investigate different ways of decoding these, among them a Maximum Likelihood decoder and estimate error rates made by these predictors, comparing to maximum rule to a linear rule.

References

  1. Pouget A, Dayan P, Zemel R: Information processing with population codes.

    Nature Reviews Neuroscience 2000, 1(2):125-132. PubMed Abstract | Publisher Full Text OpenURL

  2. Averbeck B, Latham P, Pouget A: Neural correlations, population coding and computation.

    Nature reviews Neuroscience 2006, 7(5):358-66.

    doi:10.1038/nrn1888

    PubMed Abstract | Publisher Full Text OpenURL

  3. Treue S, Hol K, Rauber H: Seeing multiple directions of motion-physiology and psychophysics.

    Nature neuroscience 2000, 3(3):270-6.

    doi:10.1038/72985

    PubMed Abstract | Publisher Full Text OpenURL

  4. Zemel R, Dayan P: Distributional population codes and multiple motion models.

    Advances in Neural Information Processing Systems 1999., 11 OpenURL

  5. Gawne T, Martin J: Responses of primate visual cortical neurons to stimuli presented by flash, saccade, blink, and external darkening.

    Journal of neurophysiology 2002, 88(5):2178-86. PubMed Abstract | Publisher Full Text OpenURL

  6. Lampl I, Ferster D, Poggio T, Riesenhuber M: Intracellular measurements of spatial integration and the MAX operation in complex cells of the cat primary visual cortex.

    Journal of neurophysiology 2004, 92(5):2704-13.

    doi:10.1152/jn.00060.2004

    PubMed Abstract | Publisher Full Text OpenURL

  7. Oleksiak A, Klink P, Postma A, van der Ham I, Lankheet M, van Wezel R: Spatial summation in macaque parietal area 7a follows a winner-take-all rule.

    Journal of neurophysiology 2011, 105(3):1150-8.

    doi:10.1152/jn.00907.2010

    PubMed Abstract | Publisher Full Text OpenURL