@ARTICLE{
  
 AUTHOR={Giacomo Indiveri  and  Bernabe Linares-Barranco  and  Tara Julia Hamilton  and  Andr? van Schaik  and  Ralph Etienne-Cummings  and  Tobi Delbruck  and  Shih-Chii Liu  and  Piotr Dudek  and  Philipp H?fliger  and  Sylvie Renaud  and  Johannes Schemmel  and  Gert Cauwenberghs  and  John Arthur  and  Kai Hynna  and  Fopefolu Folowosele  and  Sylvain SA?GHI  and  Teresa Serrano-Gotarredona  and  Jayawan Wijekoon  and  Yingxue Wang  and  Kwabena Boahen},     
     
TITLE={Neuromorphic silicon neuron circuits},      
    
JOURNAL={Frontiers in Neuroscience},      
    
VOLUME={5},      
    
YEAR={2011},      
    
NUMBER={0},     
      
URL={http://www.frontiersin.org/Journal/Abstract.aspx?s=755&name=neuromorphic engineering&ART_DOI=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.}}