Experimental Evaluation of Learning in a Neural Microsystem

Abstract

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.