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.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
OA
Restricted Neftci_etal11.pdf 1.25 MB
Autor*in
Neftci, E.; Chicca, ElisabettaUniBi ; Indiveri, G.; Douglas, R. J.
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
2011
Zeitschriftentitel
Neural Computation
Band
23
Ausgabe
10
Seite(n)
2457-2497
ISSN
0899-7667
eISSN
1530-888X
Page URI
https://pub.uni-bielefeld.de/record/2426566

Zitieren

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. https://doi.org/10.1162/NECO_a_00182
Neftci, E., Chicca, Elisabetta, Indiveri, G., and Douglas, R. J. 2011. “A systematic method for configuring VLSI networks of spiking neurons”. Neural Computation 23 (10): 2457-2497.
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.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
Dieses Objekt ist durch das Urheberrecht und/oder verwandte Schutzrechte geschützt. [...]
Volltext(e)
Access Level
OA Open Access
Zuletzt Hochgeladen
2019-09-06T08:57:58Z
MD5 Prüfsumme
69e833c1db4fbdae97854a3f14b9023b
Name
Neftci_etal11.pdf 1.25 MB
Access Level
Restricted Closed Access
Zuletzt Hochgeladen
2019-09-06T08:57:58Z
MD5 Prüfsumme
8b7e4fc776c6021f84de15cb1b698c01


20 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks.
Rutishauser U, Slotine JJ, Douglas RJ., Neural Comput 30(5), 2018
PMID: 29566357
ASIC Implementation of a Nonlinear Dynamical Model for Hippocampal Prosthesis.
Qiao Z, Han Y, Han X, Xu H, Li WXY, Song D, Berger TW, Cheung RCC., Neural Comput 30(9), 2018
PMID: 29949460
Hodgkin-Huxley Neuron and FPAA Dynamics.
Natarajan A, Hasler J., IEEE Trans Biomed Circuits Syst 12(4), 2018
PMID: 30010587
Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields.
Kreiser R, Aathmani D, Qiao N, Indiveri G, Sandamirskaya Y., Front Neurosci 12(), 2018
PMID: 30524218
Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System.
Milde MB, Blum H, Dietmüller A, Sumislawska D, Conradt J, Indiveri G, Sandamirskaya Y., Front Neurorobot 11(), 2017
PMID: 28747883
Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems.
Broccard FD, Joshi S, Wang J, Cauwenberghs G., J Neural Eng 14(4), 2017
PMID: 28573983
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
PMID: 29240769
Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.
Stromatias E, Neil D, Pfeiffer M, Galluppi F, Furber SB, Liu SC., Front Neurosci 9(), 2015
PMID: 26217169
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
Six networks on a universal neuromorphic computing substrate.
Pfeil T, Grübl A, Jeltsch S, Müller E, Müller P, Petrovici MA, Schmuker M, Brüderle D, Schemmel J, Meier K., Front Neurosci 7(), 2013
PMID: 23423583
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

80 References

Daten bereitgestellt von Europe PubMed Central.


AUTHOR UNKNOWN, 0
Estimates of the net excitatory currents evoked by visual stimulation of identified neurons in cat visual cortex.
Ahmed B, Anderson JC, Douglas RJ, Martin KA, Whitteridge D., Cereb. Cortex 8(5), 1998
PMID: 9722089

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Synchrony in silicon: the gamma rhythm.
Arthur JV, Boahen KA., IEEE Trans Neural Netw 18(6), 2007
PMID: 18051195

AUTHOR UNKNOWN, 0
Synaptic dynamics in analog VLSI.
Bartolozzi C, Indiveri G., Neural Comput 19(10), 2007
PMID: 17716003

AUTHOR UNKNOWN, 0
Theory of orientation tuning in visual cortex.
Ben-Yishai R, Bar-Or RL, Sompolinsky H., Proc. Natl. Acad. Sci. U.S.A. 92(9), 1995
PMID: 7731993

AUTHOR UNKNOWN, 0

Brüderle, 2009

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
PyNN: A Common Interface for Neuronal Network Simulators.
Davison AP, Bruderle D, Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L, Yger P., Front Neuroinform 2(), 2008
PMID: 19194529

Dayan, 2001

Destexhe, 1998

Douglas, 1995

Douglas, 1994
Neuronal circuits of the neocortex.
Douglas RJ, Martin KA., Annu. Rev. Neurosci. 27(), 2004
PMID: 15217339

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Dynamics of the firing probability of noisy integrate-and-fire neurons.
Fourcaud N, Brunel N., Neural Comput 14(9), 2002
PMID: 12184844
Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit.
Hahnloser RH, Sarpeshkar R, Mahowald MA, Douglas RJ, Seung HS., Nature 405(6789), 2000
PMID: 10879535
Permitted and forbidden sets in symmetric threshold-linear networks.
Hahnloser RH, Seung HS, Slotine JJ., Neural Comput 15(3), 2003
PMID: 12620160

Hansel, 1998
The NEURON simulation environment.
Hines ML, Carnevale NT., Neural Comput 9(6), 1997
PMID: 9248061
Efficient estimation of detailed single-neuron models.
Huys QJ, Ahrens MB, Paninski L., J. Neurophysiol. 96(2), 2006
PMID: 16624998
Space-rate coding in an adaptive silicon neuron.
Hynna K, Boahen K., Neural Netw 14(6-7), 2001
PMID: 11665760

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity.
Indiveri G, Chicca E, Douglas R., IEEE Trans Neural Netw 17(1), 2006
PMID: 16526488
Predicting every spike: a model for the responses of visual neurons.
Keat J, Reinagel P, Reid RC, Meister M., Neuron 30(3), 2001
PMID: 11430813
Silicon auditory processors as computer peripherals.
Lazzaro J, Wawrzynek J, Mahowald M, Sivilotti M, Gillespie D., IEEE Trans Neural Netw 4(3), 1993
PMID: 18267754

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
A silicon neuron.
Mahowald M, Douglas R., Nature 354(6354), 1991
PMID: 1661852

Mahowald, 1989

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

Neftci, 2008

Neftci, 2010
Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model.
Paninski L, Pillow JW, Simoncelli EP., Neural Comput 16(12), 2004
PMID: 15516273

AUTHOR UNKNOWN, 0

Pecevski, Frontiers Neuroinf. 3(), 2008

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

Orchard, 2007
State-dependent computation using coupled recurrent networks.
Rutishauser U, Douglas RJ., Neural Comput 21(2), 2009
PMID: 19431267

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

Schwartz, 1993
CAVIAR: a 45k neuron, 5M synapse, 12G connects/s AER hardware sensory-processing- learning-actuating system for high-speed visual object recognition and tracking.
Serrano-Gotarredona R, Oster M, Lichtsteiner P, Linares-Barranco A, Paz-Vicente R, Gomez-Rodriguez F, Camunas-Mesa L, Berner R, Rivas-Perez M, Delbruck T, Liu SC, Douglas R, Hafliger P, Jimenez-Moreno G, Civit Ballcels A, Serrano-Gotarredona T, Acosta-Jimenez AJ, Linares-Barranco B., IEEE Trans Neural Netw 20(9), 2009
PMID: 19635693
Rate models for conductance-based cortical neuronal networks.
Shriki O, Hansel D, Sompolinsky H., Neural Comput 15(8), 2003
PMID: 14511514
Neurotech for neuroscience: unifying concepts, organizing principles, and emerging tools.
Silver R, Boahen K, Grillner S, Kopell N, Olsen KL., J. Neurosci. 27(44), 2007
PMID: 17978017
A multiconductance silicon neuron with biologically matched dynamics.
Simoni MF, Cymbalyuk GS, Sorensen ME, Calabrese RL, DeWeerth SP., IEEE Trans Biomed Eng 51(2), 2004
PMID: 14765707
Belief propagation in networks of spiking neurons.
Steimer A, Maass W, Douglas R., Neural Comput 21(9), 2009
PMID: 19548806

Tsividis, 1998

AUTHOR UNKNOWN, 0
Building blocks for electronic spiking neural networks.
van Schaik A., Neural Netw 14(6-7), 2001
PMID: 11665758

AUTHOR UNKNOWN, 0
Compact silicon neuron circuit with spiking and bursting behaviour.
Wijekoon JH, Dudek P., Neural Netw 21(2-3), 2007
PMID: 18262751

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®
Quellen

PMID: 21732859
PubMed | Europe PMC

Suchen in

Google Scholar