Abstract
Artificial neural networks learn by adapting interconnection weights. A generalised weight adaptation expression for associative learning has been implemented using synapse circuits based on floating gate devices. A reinforcement depending on the correlation of a synapse input and a neuronal output is used. The circuits also illustrate the influence of the conditioning stimuli amplitude on the conditioning rate.
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