Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-24T14:48:13.189Z Has data issue: false hasContentIssue false
  • English
  • Français

Our understanding of neural codes rests on Shannon's foundations

Published online by Cambridge University Press:  28 November 2019

Charles R. Gallistel Open the ORCID record for Charles R. Gallistel [Opens in a new window]
Charles R. Gallistel*
Affiliation:
Rutgers Center for Cognitive Science, Rutgers University, Piscataway, NJ08854. [email protected]://ruccs.rutgers.edu/gallistel-research-interests
Get access Rights & Permissions [Opens in a new window]

Abstract

Shannon's theory lays the foundation for understanding the flow of information from world into brain: There must be a set of possible messages. Brain structure determines what they are. Many messages convey quantitative facts (distances, directions, durations, etc.). It is impossible to consider how neural tissue processes these numbers without first considering how it encodes them.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 42 , 2019 , e226
Check for updates
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Boker, S. M. (1997) A measurement of the adaptation of color vision to the spectral environment. Psychological Science 8(2):130–34.CrossRefGoogle Scholar
Dyer, F. C. & Dickinson, J. A. (1996) Sun-compass learning in insects: Representation in a simple mind. Current Directions in Psychological Science 5:67–72.CrossRefGoogle Scholar
Gallistel, C.R. (2017). The coding question. Trends in Cognitive Science 21(7):498–508.CrossRefGoogle ScholarPubMed
Gallistel, C. R. (2018) Finding numbers in the brain. Proceedings of the Royal Society London. B Biological Sciences 373(1740):20170119. Available at: https://royalsocietypublishing.org/doi/10.1098/rstb.2017.0119.CrossRefGoogle Scholar
Gallistel, C. R. (in press) Where meanings arise and how: Building on Shannon's foundations. Mind and Language (invited submission to a special issue on representation).Google Scholar
Gallistel, C. R. & King, A. P. (2010) Memory and the computational brain: Why cognitive science will transform neuroscience. Wiley/Blackwell.Google Scholar
Judson, H. (1980) The eighth day of creation. Simon & Schuster.Google Scholar
Maloney, L.T. (2003) Surface colour perception and environmental constraints. In: Colour perception: Mind and the physical world, ed. Masufeld, R. & Heyer, D.. New York: Oxford.Google Scholar
Menzel, R., Kirbach, A., Hass, W.-D., Fischer, B., Fuchs, J., Koblofsky, M., Lehmann, K., Reiter, L., Meyer, H., Nguyen, H., Jones, S., Norton, P. & Greggers, U. (2011) A common frame of reference for learned and communicated vectors in honeybee navigation. Current Biology 21:645–50.CrossRefGoogle ScholarPubMed
Qian, N. & Zhang, J. (2019) Neuronal firing rate as code length: A hypothesis. Computational Brain and Behavior. Available at: https://doi.org/10.1007/s42113-019-00028-z.CrossRefGoogle Scholar
Rieke, F., Warland, D., van Stevenick R., de Ruyter & Bialek, W. (1997) Spikes: Exploring the neural code. MIT Press.Google Scholar
Shannon, C. E. (1948) A mathematical theory of communication. Bell Systems Technical Journal 27(3):379–423, 623–56. Available at: https://archive.org/details/bellsystemtechni27amerrich/page/379.CrossRefGoogle Scholar
Stevens, C. F. (2018) Conserved features of the primate face code. Proceedings of the National Academy of Sciences USA 115(3):584–8. doi:10.1073/pnas.1716341115.CrossRefGoogle ScholarPubMed
von Frisch, K. (1967) The dance-language and orientation of bees. Harvard University Press.Google Scholar
von Frisch, K. & Lindauer, M. (1954) Himmel und Erde in Konkurrenz bei der Orientierung der Bienen. Die Naturwissenschaften 41(1 June):245–53.CrossRefGoogle Scholar