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A CNN framework for modeling parallel processing in a mammalian retina
Author(s) -
Bálya Dávid,
Roska Botond,
Roska Tamás,
Werblin Frank S.
Publication year - 2002
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.204
Subject(s) - retina , computer science , cellular neural network , set (abstract data type) , biological neural network , universal turing machine , feature (linguistics) , layer (electronics) , artificial intelligence , parallel processing , artificial neural network , algorithm , neuroscience , parallel computing , machine learning , biology , computation , linguistics , philosophy , chemistry , organic chemistry , turing machine , programming language
We present here a simple multi‐layer cellular neural/non‐linear network (CNN) model of the mammalian retina, capable of implementation on CNN Universal Machine (CNN‐UM) chips. The basis of the model is a simple multi‐layer cellular neural/non‐linear Network ( IEEE Trans. Circuits Systems 1988; 35 :1257; IEEE Trans. Circuits Systems 1993; 40 :147). The characterization of the elements in the CNN model is based on anatomical and physiological studies performed in the rabbit retina. The living mammalian retina represents the visual world in a set of about a dozen different ‘feature detecting’ parallel representations ( Nature 2001; 410 :583–587). Our CNN model is capable of reproducing qualitatively the same full set of space–time patterns as the living retina in response to a flashed square. The modelling framework can then be used to predict the set of retinal responses to more complex patterns and is also applicable to studies of the other biological sensory systems. The work represents a major step forward in the complexity and programmability of retinal models. Copyright © 2002 John Wiley & Sons, Ltd.