Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Analog cmos implementation of artificial neural networks for temporal signal learning
Analog cmos implementation of artificial neural networks for temporal signal learning
An analog VLSI recurrent neural network learning a continuous-time trajectory
IEEE Transactions on Neural Networks
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The design and analog VLSI implementation of a recurrent neural network with integrated temporal learning is presented. The learning algorithm is forward in time, and is implemented strictly as instantaneous, local weight updates. PSpice simulations of networks with 4 to 6 neurons demonstrate robust learning of trajectory generation and classification tasks. A scalable 2-D VLSI architecture is described and a prototupe 4-neuron recurrent neural network with learning has subsequently been fabricated in MOSIS TinyChip 2 micron technology. Experimental results of the chip validate the learning performance with convergence in the millisecond range. Specific experimental results of learning circular and figure-8 dynamic trajectories are included.