A VLSI neuromorphic device for implementing spike-based neural networks

Abstract

We present a neuromorphic VLSI device which comprises hybrid ana- log/digital circuits for implementing networks of spiking neurons. Each neuron in- tegrates input currents from a row of multiple analog synaptic circuit. The synapses integrate incoming spikes, and produce output currents which have temporal dy- namics analogous to those of biological post synaptic currents. The VLSI device can be used to implement real-time models of cortical networks, as well as real-time learning and classification tasks. We describe the chip architecture and the analog circuits used to implement the neurons and synapses. We describe the functionality of these circuits and present experimental results demonstrating the network level functionality.