The construction of the world's largest functional brain model and large-scale, real-time hardware simulations rely on the same mathematical and neuromorphic methods.

A programmable chip can replace the ability of the cerebellum to learn a timed eye-blink response to a sound.

Understanding the computational principles used by the brain and how they are physically embodied is crucial for developing novel computing paradigms and guiding a new generation of technologies that can combine the strengths of industrial-scale electronics with the computational performance of brains.

A simple learning rule that implements a spatial map can be used for online correction of position estimates in a neuromorphic head direction cell system.

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.