A MOS circuit for a nonmonotonic neural network with excellent retrieval capabilities

An MOS circuit is proposed for implementing a nonmonotonic transfer characteristic of a neural network. The present research is motivated by the recent results of theoretical studies showing excellent equilibrium properties of networks with the nonmonotonic neural units. These properties include enhancement of storage capacity and complete elimination of noise in associative memory recall. The simple form of the transfer characteristic enables one to implement it with a simple electrical circuit of standard MOS transistors. SPICE simulation results are shown for the behavior of the neural units in associative memory recall.

[1]  Sompolinsky,et al.  Storing infinite numbers of patterns in a spin-glass model of neural networks. , 1985, Physical review letters.

[2]  M. Shiino,et al.  Onset of 'super retrieval phase' and enhancement of the storage capacity in neural networks of nonmonotonic neurons , 1993 .

[3]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Carver Mead,et al.  Analog VLSI and neural systems , 1989 .

[5]  Tomoki Fukai,et al.  Retrieval properties of analog neural networks and the nonmonotonicity of transfer functions , 1995, Neural Networks.

[6]  Josef A. Nossek,et al.  An analog implementation of discrete-time cellular neural networks , 1992, IEEE Trans. Neural Networks.

[7]  Shiino,et al.  Self-consistent signal-to-noise analysis of the statistical behavior of analog neural networks and enhancement of the storage capacity. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[8]  Masahiko Morita,et al.  Associative memory with nonmonotone dynamics , 1993, Neural Networks.

[9]  Mohammed Ismail,et al.  Analog VLSI Implementation of Neural Systems , 2011, The Kluwer International Series in Engineering and Computer Science.

[10]  Boes,et al.  Statistical mechanics for networks of graded-response neurons. , 1991, Physical review. A, Atomic, molecular, and optical physics.

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

[12]  M. Shiino,et al.  Replica-symmetric theory of nonlinear analogue neural networks , 1990 .