AUTHOR=Indiveri Giacomo , Linares-Barranco Bernabe , Hamilton Tara J., van Schaik André , Etienne-Cummings Ralph , Delbruck Tobi , Liu Shih-Chii , Dudek Piotr , Häfliger Philipp , Renaud Sylvie , Schemmel Johannes , Cauwenberghs Gert , Arthur John , Hynna Kai , Folowosele Fopefolu , SAÏGHI Sylvain , Serrano-Gotarredona Teresa , Wijekoon Jayawan , Wang Yingxue , Boahen Kwabena TITLE=Neuromorphic Silicon Neuron Circuits JOURNAL=Frontiers in Neuroscience VOLUME=5 YEAR=2011 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2011.00073 DOI=10.3389/fnins.2011.00073 ISSN=1662-453X 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.