Neuromorphic circuit implementation of isotropic sequence order learning

Yang, Zhijun ORCID: https://orcid.org/0000-0003-2615-4297 and Murray, Alan (2010) Neuromorphic circuit implementation of isotropic sequence order learning. 2010 International Conference on Artificial Intelligence and Computational Intelligence. In: International Conference on Artificial Intelligence and Computational Intelligence (AICI 2010), 23-24 Oct 2010, Sanya, China. ISBN 9781424484324. [Conference or Workshop Item] (doi:10.1109/AICI.2010.182)

Abstract

The isotropic sequence order (ISO) learning is an improved version of differential Hebbian learning algorithm. It uses a switch to turn on or off the learning at appropriate time instants to minimise the level of inherent instability possessed by the classical Hebbian learning. In this paper we present a novel analog very large scale integrated circuit (aVLSI) model to implement ISO learning. The circuit includes an integrate-and-fire (IF) neuron, two synapses and associated low-pass filters. By adjusting a set of input biases, the Cadence simulation results show that the predictive pathway of the circuit can effectively learn the inputs of the reflexive pathway in a fast and stable process.

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