A systematic method for configuring VLSI networks of spiking neurons

Neftci E, Chicca E, Indiveri G, Douglas RJ (2011)
Neural Computation 23(10): 2457-2497.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
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Abstract / Bemerkung
An increasing number of research groups are developing custom hybrid analog/digital very large scale integration (VLSI) chips and systems that implement hundreds to thousands of spiking neurons with biophysically realistic dynamics, with the intention of emulating brainlike real-world behavior in hardware and robotic systems rather than simply simulating their performance on general-purpose digital computers. Although the electronic engineering aspects of these emulation systems is proceeding well, progress toward the actual emulation of brainlike tasks is restricted by the lack of suitable high-level configuration methods of the kind that have already been developed over many decades for simulations on general-purpose computers. The key difficulty is that the dynamics of the CMOS electronic analogs are determined by transistor biases that do not map simply to the parameter types and values used in typical abstract mathematical models of neurons and their networks. Here we provide a general method for resolving this difficulty. We describe a parameter mapping technique that permits an automatic configuration of VLSI neural networks so that their electronic emulation conforms to a higher-level neuronal simulation. We show that the neurons configured by our method exhibit spike timing statistics and temporal dynamics that are the same as those observed in the software simulated neurons and, in particular, that the key parameters of recurrent VLSI neural networks (e. g., implementing soft winner-take-all) can be precisely tuned. The proposed method permits a seamless integration between software simulations with hardware emulations and intertranslatability between the parameters of abstract neuronal models and their emulation counterparts. Most important, our method offers a route toward a high-level task configuration language for neuromorphic VLSI systems.
Erscheinungsjahr
Zeitschriftentitel
Neural Computation
Band
23
Zeitschriftennummer
10
Seite
2457-2497
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Neftci E, Chicca E, Indiveri G, Douglas RJ. A systematic method for configuring VLSI networks of spiking neurons. Neural Computation. 2011;23(10):2457-2497.
Neftci, E., Chicca, E., Indiveri, G., & Douglas, R. J. (2011). A systematic method for configuring VLSI networks of spiking neurons. Neural Computation, 23(10), 2457-2497. doi:10.1162/NECO_a_00182
Neftci, E., Chicca, E., Indiveri, G., and Douglas, R. J. (2011). A systematic method for configuring VLSI networks of spiking neurons. Neural Computation 23, 2457-2497.
Neftci, E., et al., 2011. A systematic method for configuring VLSI networks of spiking neurons. Neural Computation, 23(10), p 2457-2497.
E. Neftci, et al., “A systematic method for configuring VLSI networks of spiking neurons”, Neural Computation, vol. 23, 2011, pp. 2457-2497.
Neftci, E., Chicca, E., Indiveri, G., Douglas, R.J.: A systematic method for configuring VLSI networks of spiking neurons. Neural Computation. 23, 2457-2497 (2011).
Neftci, E., Chicca, Elisabetta, Indiveri, G., and Douglas, R. J. “A systematic method for configuring VLSI networks of spiking neurons”. Neural Computation 23.10 (2011): 2457-2497.
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2012-01-22T15:20:42Z

13 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

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Milde MB, Blum H, Dietmüller A, Sumislawska D, Conradt J, Indiveri G, Sandamirskaya Y., Front Neurorobot 11(), 2017
PMID: 28747883
Specific excitatory connectivity for feature integration in mouse primary visual cortex.
Muir DR, Molina-Luna P, Roth MM, Helmchen F, Kampa BM., PLoS Comput Biol 13(12), 2017
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Stromatias E, Neil D, Pfeiffer M, Galluppi F, Furber SB, Liu SC., Front Neurosci 9(), 2015
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PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems.
Stefanini F, Neftci EO, Sheik S, Indiveri G., Front Neuroinform 8(), 2014
PMID: 25232314
A learning-enabled neuron array IC based upon transistor channel models of biological phenomena.
Brink S, Nease S, Hasler P, Ramakrishnan S, Wunderlich R, Basu A, Degnan B., IEEE Trans Biomed Circuits Syst 7(1), 2013
PMID: 23853281
Synthesizing cognition in neuromorphic electronic systems.
Neftci E, Binas J, Rutishauser U, Chicca E, Indiveri G, Douglas RJ., Proc Natl Acad Sci U S A 110(37), 2013
PMID: 23878215
Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System.
Sheik S, Coath M, Indiveri G, Denham SL, Wennekers T, Chicca E., Front Neurosci 6(), 2012
PMID: 22347163
VLSI circuits implementing computational models of neocortical circuits.
Wijekoon JH, Dudek P., J Neurosci Methods 210(1), 2012
PMID: 22342970
Dynamic state and parameter estimation applied to neuromorphic systems.
Neftci EO, Toth B, Indiveri G, Abarbanel HD., Neural Comput 24(7), 2012
PMID: 22428591
Parameter estimation of a spiking silicon neuron.
Russell A, Mazurek K, Mihalaş S, Niebur E, Etienne-Cummings R., IEEE Trans Biomed Circuits Syst 6(2), 2012
PMID: 23852978
Robust Working Memory in an Asynchronously Spiking Neural Network Realized with Neuromorphic VLSI.
Giulioni M, Camilleri P, Mattia M, Dante V, Braun J, Del Giudice P., Front Neurosci 5(), 2011
PMID: 22347151
Tunable neuromimetic integrated system for emulating cortical neuron models.
Grassia F, Buhry L, Lévi T, Tomas J, Destexhe A, Saïghi S., Front Neurosci 5(), 2011
PMID: 22163213

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