Neural Networks — Achievements, Prospects, Difficulties

There seems to be an incessant wave of attempts to construct models which capture essential features of the human central nervous system. The types of features one has been after are: pattern recognition; classification; learning; extraction of concepts or rules from instances; adaptive computation, etc. The perceptron has been the most influential idea so far. It ruled for about 12 years, between 1957–1969, from the proposal by Rosenblatt [1], to its fascinating demise in the hands of Minsky and Pappert [2]. This phase did not touch the world of physics.

[1]  D. Amit,et al.  Statistical mechanics of neural networks near saturation , 1987 .

[2]  Stanislas Dehaene,et al.  Networks of Formal Neurons and Memory Palimpsests , 1986 .

[3]  S. Grossberg,et al.  Some physiological and biochemical consequences of psychological postulates. , 1968, Proceedings of the National Academy of Sciences of the United States of America.

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

[5]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .

[6]  Teuvo Kohonen,et al.  Representation of Associated Data by Matrix Operators , 1973, IEEE Transactions on Computers.

[7]  A. Crisanti,et al.  Saturation Level of the Hopfield Model for Neural Network , 1986 .

[8]  Gérard Weisbuch,et al.  Scaling laws for the attractors of Hopfield networks , 1985 .

[9]  Sompolinsky,et al.  Spin-glass models of neural networks. , 1985, Physical review. A, General physics.

[10]  N. Metropolis,et al.  The Monte Carlo method. , 1949 .

[11]  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.

[12]  S Dehaene,et al.  Spin glass model of learning by selection. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[13]  I. Guyon,et al.  Information storage and retrieval in spin-glass like neural networks , 1985 .

[14]  Sompolinsky,et al.  Neural networks with nonlinear synapses and a static noise. , 1986, Physical review. A, General physics.

[15]  C. Malsburg,et al.  Statistical Coding and Short-Term Synaptic Plasticity: A Scheme for Knowledge Representation in the Brain , 1986 .

[16]  Professor Moshe Abeles,et al.  Local Cortical Circuits , 1982, Studies of Brain Function.

[17]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[18]  Françoise Fogelman-Soulié,et al.  Disordered Systems and Biological Organization , 1986, NATO ASI Series.

[19]  Geoffrey E. Hinton,et al.  Separating Figure from Ground with a Parallel Network , 1986, Perception.

[20]  I. Morgenstern,et al.  Heidelberg Colloquium on Spin Glasses , 1983 .

[21]  E. Caianiello.  Outline of a theory of thought-processes and thinking machines. , 1961, Journal of theoretical biology.

[22]  Kanter,et al.  Associative recall of memory without errors. , 1987, Physical review. A, General physics.

[23]  Kanter,et al.  Temporal association in asymmetric neural networks. , 1986, Physical review letters.

[24]  J. W. Clark,et al.  Brain without mind: Computer simulation of neural networks with modifiable neuronal interactions , 1985 .

[25]  M. Virasoro.  Ultrametricity, Hopfield Model and all that , 1986 .