AUTHOR={Giacomo Indiveri and Bernabe Linares-Barranco and Tara Julia Hamilton and Andr? van Schaik and Ralph Etienne-Cummings and Tobi Delbruck and Shih-Chii Liu and Piotr Dudek and Philipp H?fliger and Sylvie Renaud and Johannes Schemmel and Gert Cauwenberghs and John Arthur and Kai Hynna and Fopefolu Folowosele and Sylvain SA?GHI and Teresa Serrano-Gotarredona and Jayawan Wijekoon and Yingxue Wang and Kwabena Boahen},

TITLE={Neuromorphic silicon neuron circuits},

JOURNAL={Frontiers in Neuroscience},




URL={http://www.frontiersin.org/Journal/Abstract.aspx?s=755&name=neuromorphic engineering&ART_DOI=10.3389/fnins.2011.00073},



ABSTRACT={Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance based Hodgkin-Huxley models to bi-dimensional generalized adaptive Integrate and Fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.}}


3 August 2011