Experimental Evaluation of Learning in a Neural Microsystem

Part of Advances in Neural Information Processing Systems 4 (NIPS 1991)

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Joshua Alspector, Anthony Jayakumar, Stephan Luna


We report learning measurements from a system composed of a cascadable learning chip, data generators and analyzers for training pattern presentation, and an X-windows based software interface. The 32 neuron learning chip has 496 adaptive synapses and can perform Boltzmann and mean-field learning using separate noise and gain controls. We have used this system to do learning experiments on the parity and replication problem. The system settling time limits the learning speed to about 100,000 patterns per second roughly independent of system size.