Floating gate analog memory for parameter and variable storage in a learning silicon neuron

Retention of parameters and learnt synaptic weights is a central problem in the construction of neural networks. We have applied analog floating gate technology to solve these problems in the context of biologically realistic `silicon neurons´. Parameters are stored on a novel floating gate array, and synaptic weights are retained by a floating gate learning synapse, that performs on-chip learning. The latter can emulate a form of long term potentiation (LTP) and long term depression (LTD) as observed in biological neurons