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