Skip to main content

Advertisement

We’d like to understand how you use our websites in order to improve them. Register your interest.

Optimal information encoding for multiple, simultaneously presented stimuli

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. 1.

    Pouget A, Dayan P, Zemel R: Information processing with population codes. Nature Reviews Neuroscience. 2000, 1 (2): 125-132. 10.1038/35039062.

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Averbeck B, Latham P, Pouget A: Neural correlations, population coding and computation. Nature reviews Neuroscience. 2006, 7 (5): 358-66. 10.1038/nrn1888. doi:10.1038/nrn1888

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Treue S, Hol K, Rauber H: Seeing multiple directions of motion-physiology and psychophysics. Nature neuroscience. 2000, 3 (3): 270-6. 10.1038/72985. doi:10.1038/72985

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Zemel R, Dayan P: Distributional population codes and multiple motion models. Advances in Neural Information Processing Systems. 1999, 11:

    Google Scholar 

  5. 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. 10.1152/jn.00151.200.

    Article  PubMed  Google Scholar 

  6. 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. 10.1152/jn.00060.2004. doi:10.1152/jn.00060.2004

    Article  PubMed  Google Scholar 

  7. 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. 10.1152/jn.00907.2010. doi:10.1152/jn.00907.2010

    Article  PubMed  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jan Pieczkowski.

Rights and permissions

This article is published under license to 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.

Reprints and Permissions

About this article

Cite this article

Pieczkowski, J., York, L., Kotaleski, J.H. et al. Optimal information encoding for multiple, simultaneously presented stimuli. BMC Neurosci 13, P17 (2012). https://doi.org/10.1186/1471-2202-13-S1-P17

Download citation

Keywords

  • Encode Scheme
  • Noise Model
  • Theoretical Implication
  • Single Stimulus
  • Probabilistic Stimulus