Analog LSI implementation of self-learning neural networks

[1]  Osamu Fujita,et al.  A sparse memory-access neural network engine with 96 parallel data-driven processing units , 1995, Proceedings ISSCC '95 - International Solid-State Circuits Conference.

[2]  Takashi Morie,et al.  An all-analog expandable neural network LSI with on-chip backpropagation learning , 1994, IEEE J. Solid State Circuits.

[3]  Hirofumi Shinohara,et al.  A 1.2GFLOPS neural network chip exhibiting fast convergence , 1994, Proceedings of IEEE International Solid-State Circuits Conference - ISSCC '94.

[4]  Yoshihito Amemiya,et al.  A floating-gate analog memory device for neural networks , 1993 .

[5]  H. C. Card,et al.  Analog CMOS deterministic Boltzmann circuits , 1993 .

[6]  Takashi Morie,et al.  Deterministic Boltzmann Machine Learning Improved for Analog LSI Implementation , 1993 .

[7]  D. Hammerstrom,et al.  Neural networks at work , 1993, IEEE Spectrum.

[8]  H. Ishiwara Proposal of Adaptive-Learning Neuron Circuits with High Density Synapse Array of Ferroelectric Thin Films , 1993 .

[9]  Robert G. Meyer,et al.  Analysis and Design of Analog Integrated Circuits , 1993 .

[10]  Tetsuro Itakura,et al.  Neuro chips with on-chip back-propagation and/or Hebbian learning , 1992 .

[11]  Takashi Morie,et al.  Analog VLSI Implementation of Adaptive Algorithms by an Extended Hebbian Synapse Circuit , 1992 .

[12]  H. Shinohara,et al.  A refreshable analog VLSI neural network chip with 400 neurons and 40 k synapses , 1992, 1992 IEEE International Solid-State Circuits Conference Digest of Technical Papers.

[13]  Joshua Alspector,et al.  Experimental Evaluation of Learning in a Neural Microsystem , 1991, NIPS.

[14]  Pierre Baldi,et al.  Contrastive Learning and Neural Oscillations , 1991, Neural Computation.

[15]  R. Pinkham,et al.  An 11-million Transistor Neural Network Execution Engine , 1991, 1991 IEEE International Solid-State Circuits Conference. Digest of Technical Papers.

[16]  Edward A. Rietman,et al.  Back-propagation learning and nonidealities in analog neural network hardware , 1991, IEEE Trans. Neural Networks.

[17]  Carsten Peterson,et al.  Explorations of the mean field theory learning algorithm , 1989, Neural Networks.

[18]  Carsten Peterson,et al.  A Mean Field Theory Learning Algorithm for Neural Networks , 1987, Complex Syst..

[19]  Geoffrey E. Hinton,et al.  Learning symmetry groups with hidden units: beyond the perceptron , 1986 .

[20]  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.