Abstract
Humans effortlessly classify and recognize complex patterns even if their attributes are imprecise and often inconsistent. It is not clear how the brain processes uncertain visual information. We have recorded single cell responses to various visual stimuli in area V4 of the monkey’s visual cortex. Different visual patterns are described by their attributes (condition attributes) and placed, together with the decision attributes, in a decision table. Decision attributes are divided into several classes determined by the strength of the neural responses. Small cell responses are classified as class 0, medium to strong responses are classified as classes 1 to n-1 (min(n)=3 ), and the strongest cell responses are classified as class n. The higher the class of the decision attribute the more preferred is the stimulus. Therefore each cell divides stimuli into its own family of equivalent objects.
By comparing responses of different cells we have found related concept classes. However, many different cells show inconsistency between their decision rules, which may suggest that parallel different decision logics may be implemented in the brain.
Chapter PDF
Similar content being viewed by others
References
Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston, London, Dordrecht (1991)
Zadeh, L.A.: Toward a perception-based theory of probabilistic reasoning with imprecise probabilities. Journal of Statistical Planning and Inference 105, 233 (2002)
Przybyszewski, A.W., Linsay, P.S., Gaudiano, P., Wilson, C.: Basic Difference Between Brain and Computer: Integration of Asynchronous Processes Implemented as Hardware Model of the Retina. 18, 70–85 (2007)
Pollen, D.A., Przybyszewski, A.W., Rubin, M.A., Foote, W.: Spatial receptive field organization of macaque V4 neurons. Cereb Cortex 12, 601–616 (2002)
David, S.V., Hayden, B.Y., Gallant, J.L.: Spectral receptive field properties explain shape selectivity in area V4. J. Neurophysiol. 96, 3492–3505 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Przybyszewski, A.W. (2007). Rough Set Theory of Pattern Classification in the Brain. In: Ghosh, A., De, R.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2007. Lecture Notes in Computer Science, vol 4815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77046-6_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-77046-6_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77045-9
Online ISBN: 978-3-540-77046-6
eBook Packages: Computer ScienceComputer Science (R0)