Synonyms
Definition
A neuromorphic hardware system is an electronic system for information processing, based, to a certain degree, on the example of the biological nervous system. The operation of the whole system resembles the behavior of a network built from “neurons” and “synapses.” The degree of biological realism varies greatly between different research projects and targeted applications. Large-scale neuromorphic hardware projects target system sizes in the order of at least 106 synapses, but some aim for up to 1014. Therefore, scalability is an important aspect of large-scale neuromorphic hardware, which also distinguishes it from medium- and small-scale neuromorphic hardware.
Detailed Description
Since its invention by Carver Mead (Mead 1989) in the 1980s, the term neuromorphic hardware has undergone a big shift in meaning. Was it originally coined for analog circuitry mimicking biological synapses and neurons by maintaining a direct...
This is a preview of subscription content, log in via an institution.
References
Adhikari SP, Changju Y, Hyonhsuk K, Chua LO (2012) Memristor bridge synapse-based neural network and its learning. In: IEEE Trans Neural Netw Learn Syst 23(9). doi 10.1109/TNNLS.2012.2204770
Furber S, Temple S (2007) Neural systems engineering. J R Soc Interface 4:193–206
Indiveri G et al (2011) Neuromoprhic silicon neuron circuits. Front Neurosci. doi:10.3389/fnins.2011.00073
Kogge P et al (2008) Exascale computing study: technology challenges in achieving exascale systems. DARPA contract #FA8650-07-C-7724
Markram H, Gerstner W, Sjöström PJ (2012) Spike-timing-dependent plasticity: a comprehensive overview. Front Synaptic Neurosci 4:2. doi:10.3389/fnsyn.2012.00002
Mead C (1989) Analog VLSI and neural systems. Addison-Wesley Longman Publishing Co., Inc., Boston. ISBN 978-0201059922
Merolla P et al (2011) A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45 nm. In: Custom integrated circuits conference (CICC), 2011 IEEE
Millner S, Grübl A, Meier K, Schemmel J, Schwartz MO (2010) A VLSI implementation of the adaptive exponential integrate-and-fire neuron model. Adv Neural Inf Process Syst 23:1642–1650
Moore S, Fox P, Marsh S, Markettos A, Mujumdar A (2012) Bluehive – a field-programable custom computing machine for extreme-scale real-time neural network simulation. In: IEEE 20th international symposium on field-programmable custom computing machines, 2012, pp 133–140
Schemmel J et al (2010) A wafer-scale neuromorphic hardware system for large-scale neural modeling. In: Proceedings of the 2010 I.E. international symposium on circuits and systems (ISCAS) 2010, pp 1947–1950
Schemmel J, Grübl A, Meier K, Muller E (2006) Implementing synaptic plasticity in a VLSI spiking neural network model. In: Proceedings of the 2006 international joint conference on neural networks (IJCNN)
Silver R, Boahen K, Grillner N, Kopell N, Olsen K (2007) Neurotech for neuroscience: unifying concepts, organizing principles, and emerging tools. J Neurosci 27:11807–11819
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Schemmel, J. (2014). Neuromorphic Hardware, Large Scale. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_115-4
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7320-6_115-4
Received:
Accepted:
Published:
Publisher Name: Springer, New York, NY
Online ISBN: 978-1-4614-7320-6
eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences