title: 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.
authors: Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.
authors: Giacomo
authors: G
authors: Indiveri
authors: Bernabé
authors: B
authors: Linares-Barranco
authors: Tara Julia
authors: TJ
authors: Hamilton
authors: André
authors: A
authors: van Schaik
authors: Ralph
authors: R
authors: Etienne-Cummings
authors: Tobi
authors: T
authors: Delbruck
authors: Shih-Chii
authors: SC
authors: Liu
authors: Piotr
authors: P
authors: Dudek
authors: Philipp
authors: P
authors: Häfliger
authors: Sylvie
authors: S
authors: Renaud
authors: Johannes
authors: J
authors: Schemmel
authors: Gert
authors: G
authors: Cauwenberghs
authors: John
authors: J
authors: Arthur
authors: Kai
authors: K
authors: Hynna
authors: Fopefolu
authors: F
authors: Folowosele
authors: Sylvain
authors: S
authors: Saighi
authors: Teresa
authors: T
authors: Serrano-Gotarredona
authors: Jayawan
authors: J
authors: Wijekoon
authors: Yingxue
authors: Y
authors: Wang
authors: Kwabena
authors: K
authors: Boahen