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      Neuromorphic Silicon Neuron Circuits

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          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.

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          Most cited references60

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          Impulses and Physiological States in Theoretical Models of Nerve Membrane

          Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model" has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a "physiological state diagram," with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable.
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            Simple model of spiking neurons.

            A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.
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              Neuromorphic electronic systems

              C Mead (1990)
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                Author and article information

                Journal
                Front Neurosci
                Front. Neurosci
                Frontiers in Neuroscience
                Frontiers Research Foundation
                1662-4548
                1662-453X
                31 May 2011
                2011
                : 5
                : 73
                Affiliations
                [1] 1simpleInstitute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland
                [2] 2simpleNational Microelectronics Center, Instituto Microelectronica Sevilla Sevilla, Spain
                [3] 3simpleSchool of Electrical Engineering and Telecommunications, University of New South Wales Sydney, NSW, Australia
                [4] 4simpleSchool of Electrical and Information Engineering, University of Sydney Sydney, NSW, Australia
                [5] 5simpleWhiting School of Engineering, Johns Hopkins University Baltimore, MD, USA
                [6] 6simpleSchool of Electrical and Electronic Engineering, University of Manchester Manchester, UK
                [7] 7simpleDepartment of Informatics, University of Oslo Oslo, Norway
                [8] 8simpleLaboratoire de l'Intégration du Matériau au Système, Bordeaux University and IMS-CNRS Laboratory Bordeaux, France
                [9] 9simpleKirchhoff Institute for Physics, University of Heidelberg Heidelberg, Germany
                [10] 10simpleDepartment of Bioengineering and Institute for Neural Computation, University of California San Diego La Jolla, CA, USA
                [11] 11simpleStanford Bioengineering, Stanford University Stanford, CA, USA
                [12] 12simpleJanelia Farm Research Campus, Howard Hughes Medical Institute Ashburn, VA, USA
                Author notes

                Edited by: Bert Shi, The Hong Kong University of Science and Technology, Hong Kong

                Reviewed by: Theodore Yu, University of California at San Diego, USA; Chi-Sang Poon, Harvard – MIT Division of Health Sciences and Technology, USA; Tadashi Shibata, University of Tokyo, Japan

                *Correspondence: Giacomo Indiveri, Institute of Neuroinformatics, Swiss Federal Institute of Technology Zurich, University of Zurich, Zurich CH-8057, Switzerland. e-mail: giacomo@ 123456ini.phys.ethz.ch

                This article was submitted to Frontiers in Neuromorphic Engineering, a specialty of Frontiers in Neuroscience.

                Article
                10.3389/fnins.2011.00073
                3130465
                21747754
                0725b558-bc1f-4613-baf6-fa16cac21aea
                Copyright © 2011 Indiveri, Linares-Barranco, Hamilton, van Schaik, Etienne-Cummings, Delbruck, Liu, Dudek, Häfliger, Renaud, Schemmel, Cauwenberghs, Arthur, Hynna, Folowosele, Saïghi, Serrano-Gotarredona, Wijekoon, Wang and Boahen.

                This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.

                History
                : 15 December 2010
                : 07 May 2011
                Page count
                Figures: 21, Tables: 2, Equations: 3, References: 102, Pages: 23, Words: 16250
                Categories
                Neuroscience
                Review Article

                Neurosciences
                log-domain,spiking,circuit,integrate and fire,conductance based,analog vlsi,adaptive exponential,subthreshold

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