Title |
Neuromorphic Silicon Neuron Circuits
|
---|---|
Published in |
Frontiers in Neuroscience, January 2011
|
DOI | 10.3389/fnins.2011.00073 |
Pubmed ID | |
Authors |
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, Kwabena Boahen |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 20% |
Colombia | 1 | 20% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 10 | 1% |
United Kingdom | 9 | <1% |
Germany | 4 | <1% |
France | 4 | <1% |
Switzerland | 3 | <1% |
Japan | 3 | <1% |
Australia | 2 | <1% |
China | 2 | <1% |
Singapore | 1 | <1% |
Other | 2 | <1% |
Unknown | 930 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 241 | 25% |
Researcher | 150 | 15% |
Student > Master | 141 | 15% |
Student > Bachelor | 80 | 8% |
Student > Doctoral Student | 39 | 4% |
Other | 119 | 12% |
Unknown | 200 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 385 | 40% |
Computer Science | 83 | 9% |
Physics and Astronomy | 74 | 8% |
Agricultural and Biological Sciences | 57 | 6% |
Neuroscience | 47 | 5% |
Other | 109 | 11% |
Unknown | 215 | 22% |