Neuromorphic silicon neuron circuits

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.
Type de document :
Article dans une revue
Frontiers in Neuroscience, Frontiers, 2011, pp.1-23. 〈10.3389/fnins.2011.00073〉
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Contributeur : Chrystel Plumejeau <>
Soumis le : mercredi 1 juin 2011 - 15:31:26
Dernière modification le : jeudi 11 janvier 2018 - 06:21:09

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Giacomo Indiveri, Bernabé Linares-Barranco, Tara Julia Hamilton, André Van Schaik, Ralph Etienne-Cummings, et al.. Neuromorphic silicon neuron circuits. Frontiers in Neuroscience, Frontiers, 2011, pp.1-23. 〈10.3389/fnins.2011.00073〉. 〈hal-00597675〉



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