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Brain building 101: advances in large-scale neural simulation »

Chris Eliasmith

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.
 
 
Modern classical conditioning: replacing a learning circuit in the brain »

Simeon A. Bamford and Paolo Del Giudice

A programmable chip can replace the ability of the cerebellum to learn a timed eye-blink response to a sound.
 
 
Frontiers in neuromorphic engineering »

Giacomo Indiveri and Timothy K. Horiuchi

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.

 
Learning to correct orientation estimates using spatial memory »

Tarek Massoud and Timothy Horiuchi

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.
 
 
Neuromorphic silicon neuron circuits »

Giacomo Indiveri, Bernabé Linares-Barranco, Tara Julia Hamilton, André van Schaik, Ralph Etienne-Cummings, Tobi Delbruck, Shih-Chii Liu, Piotr Dudek, Philipp Häfliger, Sylvie Renaud, Johannes Schemmel, Gert Cauwenberghs, John Arthur, Kai Hynna, Fopefolu Folowosele, Sylvain Saighi, Teresa Serrano-Gotarredona, Jayawan Wijekoon, Yingxue Wang, and Kwabena Boahen

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.

 

@NeuroEng



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