In this paper we describe the most common building blocks and techniques used to implement spiking neuron circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results.

In this paper we are linking one type of memristor nanotechnology devices to the biological synaptic update rule known as spike-time-dependent-plasticity (STDP) found in real biological synapses. This allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex.

Co-developing neuromorphic integrated circuit learning models and derivations significantly increases biological accuracy and reduces circuit complexity.

A neuromorphic sound localization system is presented, based on the extraction of interaural time difference from a far-field source and employing two microphones and a pair of silicon cochleae with address event interface for front-end processing.

A mathematical framework is being used to investigate the learning dynamics induced by a class of biologically realistic synaptic plasticity rules in recurrently connected neuronal networks.