SWITCHED CAPACITOR-BASED IMPLEMENTATION OF INTEGRATE-AND-FIRE NEURAL NETWORKS

Daniel Hajtáš - Daniela Ďuračková

   This paper is dealing with an analogue implementation of an Integrate and Fire neural network consisting of the learning synapse, which is a vital part of a self-organising neural network and the neurone designed according its biological counterpart. The proposed synapse includes a post-synaptic potential forming block, which makes it possible to uniquely characterise each synapse output in a complete neural network. This approach is conceptually closer to its biological counterpart. The design uses switched capacitor technique in order to be able to make the above described modifications realisable.

Keywords: integrate and fire neurones, learning synapses, neural networks implementation, built-in learning, hebbian learning rule

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