Justification of Neural Modeling

Two different motives are discernible in neural modeling. The original one is an attempt to describe biophysical phenomena that take place in real biological neurons, whereby it may be expected that some primitives or basic elements of information processing by the brain could be isolated and identified. Another one is a direct attempt to develop new devices based on heuris-tically conceived, although biologically inspired simple components such as threshold-logic units or formal neurons. The circuits thereby designed are usually called artificial neural networks (ANNs).

[1]  Stephen Grossberg,et al.  ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures , 1990, Neural Networks.

[2]  Fanya S. Montalvo,et al.  Consensus versus Competition in Neural Networks: A Comparative Analysis of Three Models , 1975, Int. J. Man Mach. Stud..

[3]  M. Fazeli Synaptic plasticity: on the trail of the retrograde messenger , 1992, Trends in Neurosciences.

[4]  T. Geballe,et al.  The central parsec of the Galaxy , 1979 .

[5]  Roman Bek,et al.  Discourse on one way in which a quantum-mechanics language on the classical logical base can be built up , 1978, Kybernetika.

[6]  Klaus Schumacher,et al.  VLSI technologies for artificial neural networks , 1989, IEEE Micro.

[7]  C. Malsburg,et al.  How patterned neural connections can be set up by self-organization , 1976, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[8]  Stephen Grossberg,et al.  Nonlinear neural networks: Principles, mechanisms, and architectures , 1988, Neural Networks.

[9]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[10]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[11]  R. M. Gaze,et al.  The arrow model: retinotectal specificity and map formation in the goldfish visual system , 1976, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[12]  Risto Miikkulainen,et al.  Subsymbolic natural language processing - an integrated model of scripts, lexicon, and memory , 1993, Neural network modeling and connectionism.

[13]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[14]  R. M. Gaze,et al.  The Visual System and “Neuronal Specificity” , 1972, Nature.

[15]  C. V. D. Malsburg,et al.  Frank Rosenblatt: Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms , 1986 .

[16]  Jirí Benes,et al.  On neural networks , 1990, Kybernetika.

[17]  Shun-Ichi Amari,et al.  Topographic organization of nerve fields , 1979, Neuroscience Letters.

[18]  G. Edelman,et al.  The NO hypothesis: possible effects of a short-lived, rapidly diffusible signal in the development and function of the nervous system. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[19]  E. Kandel Small systems of neurons. , 1979, Scientific American.

[20]  Teuvo Kohonen,et al.  Physiological interpretationm of the self-organizing map algorithm , 1993 .

[21]  J Saarinen,et al.  Self-Organized Formation of Colour Maps in a Model Cortex , 1985, Perception.

[22]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[23]  R. Pérez,et al.  Development of Specificity in the Cat Visual Cortex , 1975, Journal of mathematical biology.

[24]  J. Sundberg The acoustics of the singing voice. , 1977 .

[25]  S. Geman SOME AVERAGING AND STABILITY RESULTS FOR RANDOM DIFFERENTIAL EQUATIONS , 1979 .

[26]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[27]  R. Didday A model of visuomotor mechanisms in the frog optic tectum , 1976 .

[28]  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.

[29]  A. Caramazza Some aspects of language processing revealed through the analysis of acquired aphasia: the lexical system. , 1988, Annual review of neuroscience.

[30]  R. Hunt,et al.  Patterning of neuronal locus specificities in retinal ganglion cells after partial extirpation of the embryonic eye , 1975 .

[31]  H. Goodglass,et al.  Category Specific Dissociations in Naming and Recognition by Aphasic Patients , 1986, Cortex.

[32]  Stephen Grossberg,et al.  The ART of adaptive pattern recognition by a self-organizing neural network , 1987, Computer.

[33]  R. Sperry CHEMOAFFINITY IN THE ORDERLY GROWTH OF NERVE FIBER PATTERNS AND CONNECTIONS. , 1963, Proceedings of the National Academy of Sciences of the United States of America.

[34]  G. Shepherd The Synaptic Organization of the Brain , 1979 .

[35]  Richard Sorabji,et al.  Aristotle on Memory , 1972 .

[36]  James A. Anderson,et al.  A simple neural network generating an interactive memory , 1972 .

[37]  P. Schönemann On artificial intelligence , 1985, Behavioral and Brain Sciences.

[38]  G. Ojemann Ojemann's data: Provocative but mysterious , 1983, Behavioral and Brain Sciences.

[39]  E. Oja Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[40]  Teuvo Kohonen,et al.  An introduction to neural computing , 1988, Neural Networks.

[41]  S. Sharma,et al.  Reformation of retinotectal projections after various tectal ablations in adult goldfish. , 1972, Experimental neurology.

[42]  T Poggio,et al.  Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.