A compact current mode neuron circuit with Gaussian taper learning capability

Abstract

In this paper, an analog current mode implementation of a neuron circuit capable of performing real Gaussian neighborhood taper learning is presented. The neuron cell is compacted with a reusable multiplier that can function as squarer and multiplier for Euclidean and topological distances calculation as well as for Gaussian function characteristics with adjustable learning rate. A four-neuron self-organizing map (SOM) with three dimensional input data is designed and simulated using CSM 0.18 mum technology to demonstrate the learning control and neighborhood adaptation. The network can process 4.55 million vectors per second with a minimum power consumption of 1.6 mW at 1.5 V.