A Spike Based Learning Neuron in Analog VLSI

Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)

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Philipp Häfliger, Misha Mahowald, Lloyd Watts


Many popular learning rules are formulated in terms of continu(cid:173) ous, analog inputs and outputs. Biological systems, however, use action potentials, which are digital-amplitude events that encode analog information in the inter-event interval. Action-potential representations are now being used to advantage in neuromorphic VLSI systems as well. We report on a simple learning rule, based on the Riccati equation described by Kohonen [1], modified for action-potential neuronal outputs. We demonstrate this learning rule in an analog VLSI chip that uses volatile capacitive storage for synaptic weights. We show that our time-dependent learning rule is sufficient to achieve approximate weight normalization and can detect temporal correlations in spike trains.

A Spike Based Learning Neuron in Analog VLSI