Efficient Biologically-based Pattern-recognizing Networks

A biologiclly-motivated classifying neural network which is based on the feature extraction scheme found in the visual cortex is suggested. A special process is proposed for grading and automatically selecting the "best" features for specific recognition tasks. Ranking is based on a feature's calculated discriminating ability, such that a given class is separated from each and every other class by a given amount. The outcome is a net with less computational complexity than other neural nets, yet one which is more biologically plausible.The main motivation for constructing a reduced net is that the complex circuitry of the brain deals with a huge number of patterns, while a machine-based recognition system usually deals with a limited number of patterns. Results show that feature reduction is drastic and that very compact nets, of the order of tens of neurons, can be used to classify patterns, even in a noisy environment. Copyright 1996 Elsevier Science Ltd

[1]  Patrick S. P. Wang,et al.  Handwritten digit recognition using neural networks , 1992, Other Conferences.

[2]  David H. Wolpert,et al.  The Relationship Between Occam's Razor and Convergent Guessing , 1990, Complex Syst..

[3]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[4]  D. Hubel,et al.  Ferrier lecture - Functional architecture of macaque monkey visual cortex , 1977, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[5]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[6]  Geoffrey E. Hinton,et al.  Parallel visual computation , 1983, Nature.

[7]  Nathan Intrator,et al.  Feature Extraction Using an Unsupervised Neural Network , 1992, Neural Computation.

[8]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[9]  Li-Min Fu.  Analysis of the dimensionality of neural networks for pattern recognition , 1990, Pattern Recognit..

[10]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[11]  I. Biederman.  Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[12]  Kunihiko Fukushima,et al.  A neural network for visual pattern recognition , 1988, Computer.

[13]  Orly Yadid-Pecht,et al.  A biologically-inspired improved MAXNET , 1995, IEEE Trans. Neural Networks.

[14]  R. L. de Valois,et al.  Relationship between spatial-frequency and orientation tuning of striate-cortex cells. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[15]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

[16]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[17]  T. Wiesel,et al.  The influence of contextual stimuli on the orientation selectivity of cells in primary visual cortex of the cat , 1990, Vision Research.

[18]  Marley M. B. R. Vellasco,et al.  Neural computing overview , 1989 .

[19]  A. Parker,et al.  Two-dimensional spatial structure of receptive fields in monkey striate cortex. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[20]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[21]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[22]  Laura Ignizio Burke.  Introduction to artificial neural systems for pattern recognition , 1991, Comput. Oper. Res..

[23]  R. L. Valois,et al.  The orientation and direction selectivity of cells in macaque visual cortex , 1982, Vision Research.

[24]  P. Lennie,et al.  Spatial frequency analysis in the visual system. , 1985, Annual review of neuroscience.

[25]  T. Wiesel,et al.  Functional architecture of macaque monkey visual cortex , 1977 .

[26]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[27]  T. Wiesel,et al.  Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[28]  Ronen Basri,et al.  Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[30]  J. Movshon,et al.  Length summation in simple cells of cat striate cortex , 1984, Vision Research.

[31]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[32]  Martin D. Levine,et al.  Vision in Man and Machine , 1985 .

[33]  Alireza Khotanzad,et al.  Rotation invariant image recognition using features selected via a systematic method , 1990, Pattern Recognit..

[34]  Eric L. Schwartz,et al.  Computational Neuroscience , 1993, Neuromethods.

[35]  D. Hubel,et al.  Uniformity of monkey striate cortex: A parallel relationship between field size, scatter, and magnification factor , 1974, The Journal of comparative neurology.

[36]  A. L. I︠A︡rbus.  Eye Movements and Vision , 1967 .

[37]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.

[38]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.