Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-28T06:15:33.595Z Has data issue: false hasContentIssue false
  • English
  • Français

Neuronal inference must be local, selective, and coordinated

Published online by Cambridge University Press:  10 May 2013

William A. Phillips
William A. Phillips*
Affiliation:
Psychology Department, University of Stirling, FK9 4LA Stirling, Scotland, United Kingdom, and Frankfurt Institute of Advanced Studies, 60438 Frankfurt am Main, Germany. [email protected]://www.psychology.stir.ac.uk/staff/staff-profiles/honorary-staff/bill-phillips
Get access Rights & Permissions [Opens in a new window]

Abstract

Life is preserved and enhanced by coordinated selectivity in local neural circuits. Narrow receptive-field selectivity is necessary to avoid the curse-of-dimensionality, but local activities can be made coherent and relevant by guiding learning and processing using broad coordinating contextual gain-controlling interactions. Better understanding of the functions and mechanisms of those interactions is therefore crucial to the issues Clark examines.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 3 , June 2013 , pp. 222 - 223
Copyright
Copyright © Cambridge University Press 2013 

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

Doherty, M. J., Campbell, N. M., Tsuji, H. & Phillips, W. A. (2010) The Ebbinghaus illusion deceives adults but not young children. Developmental Science 13:714–21. doi:10.1111/j.1467-7687.2009.00931.x.CrossRefGoogle Scholar
Doherty, M. J., Tsuji, H. & Phillips, W. A. (2008) The context-sensitivity of visual size perception varies across cultures. Perception 37:1426–33.CrossRefGoogle ScholarPubMed
Feldman, H. & Friston, K. J. (2010) Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience 4:215. doi:10.3389/fnmuh.2010.00215.Google Scholar
Fiorillo, C. D. (2012) Beyond Bayes: On the need for a unified and Jaynesian definition of probability and information within neuroscience. Information 3(2):175–203. doi:10.3390/info3020175.Google Scholar
Friston, K. J. (2010) The free-energy principle: A unified brain theory? Nature Reviews Neuroscience 11(2):127–38.Google Scholar
Jaynes, E. T. (2003) Probability theory: The logic of science. Cambridge University Press.Google Scholar
Kay, J., Floreano, D. & Phillips, W. A. (1998) Contextually guided unsupervised learning using local multivariate binary processors. Neural Networks 11:117–40.Google Scholar
Kay, J. & Phillips, W. A. (2010) Coherent Infomax as a computational goal for neural systems. Bulletin of Mathematical Biology 73:344–72. doi: 10.1007/s11538-010-9564-x.CrossRefGoogle ScholarPubMed
Körding, K. P. & König, P. (2000) Learning with two sites of synaptic integration. Network: Computation in Neural Systems 11:1–15.Google ScholarPubMed
Phillips, W. A. (2012) Self-organized complexity and coherent Infomax from the viewpoint of Jaynes's probability theory. Information 3(1):1–15. doi:10.3390/info3010001.Google Scholar
Phillips, W. A., Chapman, K. L. S. & Berry, P. D. (2004) Size perception is less context-sensitive in males. Perception 33:79–86.Google Scholar
Phillips, W. A., Kay, J. & Smyth, D. (1995) The discovery of structure by multistream networks of local processors with contextual guidance. Network: Computation in Neural Systems 6:225–46.Google Scholar
Phillips, W. A. & Silverstein, S. M. (2003) Convergence of biological and psychological perspectives on cognitive coordination in schizophrenia. Behavioral and Brain Sciences 26:65–82; discussion 82–137.Google Scholar
Phillips, W. A. & Singer, W. (1997) In search of common foundations for cortical computation. Behavioral and Brain Sciences 20:657–722.Google Scholar
Phillips, W. A., von der Malsburg, C. & Singer, W. (2010) Dynamic coordination in brain and mind. In: Strüngmann Forum Report, vol. 5: Dynamic coordination in the brain: From neurons to mind, ed. von der Malsburg, C., Phillips, W. A. & Singer, W., Chapter 1, pp. 1–24. MIT Press.Google Scholar
Spratling, M. W. (2008a) Predictive coding as a model of biased competition in visual attention. Vision Research 48(12):1391–408.Google Scholar
von der Malsburg, C., Phillips, W. A. & Singer, W., eds. (2010) Strungmann Forum Report, Vol. 5. Dynamic coordination in the brain: From neurons to mind. MIT Press.Google Scholar