Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-61188
Boahen, K; Wang, Y; Wijekoon, J; Serrano-Gotarredona, T; Saighi, S; Folowosele, F; Hynna, K; Arthur, J; Cauwenberghs, G; Schemmel, J; Renaud, S; Häfliger, P; Dudek, P; Liu, S-C; Delbruck, T; Etienne-Cummings, R; van Schaik, A; Hamilton, T J; Linares-Barranco, B; Indiveri, G (2011). Neuromorphic Silicon Neuron Circuits. Frontiers in Neuroscience, 5:73.
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.
|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||07 Faculty of Science > Institute of Neuroinformatics|
|DDC:||570 Life sciences; biology|
|Uncontrolled Keywords:||Analog VLSI;Subthreshold;Spiking;Integrate and fire;Conductance based;Adaptive exponential;Log-domain;Circuit|
|Date:||5 May 2011|
|Deposited On:||12 Mar 2012 11:36|
|Last Modified:||29 Nov 2012 06:20|
|Publisher:||Frontiers Research Foundation|
Scopus®. Citation Count: 92
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