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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: May 2018
Next issue: Aug 2018
Avg review time: 108 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  4/2011 - 5

A Neuron Model for FPGA Spiking Neuronal Network Implementation

TIGAERU, L. See more information about TIGAERU, L. on SCOPUS See more information about TIGAERU, L. on IEEExplore See more information about TIGAERU, L. on Web of Science, BONTEANU, G. See more information about BONTEANU, G. on SCOPUS See more information about BONTEANU, G. on SCOPUS See more information about BONTEANU, G. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (501 KB) | Citation | Downloads: 1,419 | Views: 3,180

Author keywords
spiking neural network, neuromorphics, biological system modeling, field programmable gate arrays, very large scale integration

References keywords
neural(10), networks(9), spiking(7), membrane(5), huxley(5), hodgkin(5), neurons(4), loligo(4), link(4), giant(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2011-11-30
Volume 11, Issue 4, Year 2011, On page(s): 29 - 36
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.04005
Web of Science Accession Number: 000297764500005
SCOPUS ID: 84856609800

Abstract
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We propose a neuron model, able to reproduce the basic elements of the neuronal dynamics, optimized for digital implementation of Spiking Neural Networks. Its architecture is structured in two major blocks, a datapath and a control unit. The datapath consists of a membrane potential circuit, which emulates the neuronal dynamics at the soma level, and a synaptic circuit used to update the synaptic weight according to the spike timing dependent plasticity (STDP) mechanism. The proposed model is implemented into a Cyclone II-Altera FPGA device. Our results indicate the neuron model can be used to build up 1K Spiking Neural Networks on reconfigurable logic suport, to explore various network topologies.


References | Cited By  «-- Click to see who has cited this paper

[1] C. Koch, "Biophysics of Computation: Information Processing in Single Neurons", New York: Oxford Univ. Press., 1999

[2] W. Maass, "The Third Generation of Neural Network Models", Technische Universitat Graz, 1997.

[3] A. L. Hodgkin, F. Huxley, B. Katz, "Measurements of current-voltage relations in the membrane of the giant axon of Loligo". J. Physiol. vol. 116, pp. 424-448, 1952. [PubMed]

[4] A. L. Hodgkin, F. Huxley, "Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo". J. Physiol. vol. 116, pp. 449-472, 1952. [PubMed]

[5] A. L. Hodgkin, F. Huxley, "The components of membrane conductance in the giant axon of Loligo", J. Physiol. vol. 116, pp. 473-496, 1952. [PubMed]

[6] A. L. Hodgkin, F. Huxley, "The dual effect of membrane potential on sodium conductance in the giant axon of Loligo", J. Physiol. vol. 116 pp. 497-506, 1952. [PubMed]

[7] A. L. Hodgkin, F. Huxley, "A quantitative description of membrane current and its application to conduction and excitation in nerve", J. Physiol. vol. 116, pp. 507-544, 1952. [PubMed]

[8] E. M. Izhikevich, "Simple Model of Spiking Neurons", IEEE Transactions of Neural Networks, vol. 14, no. 6, pp. 1569-1572, 2003.
[CrossRef] [Web of Science Times Cited 1305] [SCOPUS Times Cited 1558]


[9] L. F. Abbott, "Lapique's introduction of the integrate-and-fire model neuron (1907)", Brain Research Bulletin, vol. 50, no. 5/6, pp. 303-304, 1999.

[10] K. M Hynna, K. Boahen, "Thermodynamically Equivalent Silicon Models of Voltage-Dependent Ion Channels", Neural Computation, vol. 19, no. 2, pp. 327-350, 2007

[11] J. H. B. Wijekoon, P. Dudek, "Compact Silicon Neuron with Spiking and Bursting Behaviour", Neural Networks, vol. 21, pp. 524-534, 2008.

[12] G. Indiveri, E. Chicca, R. Douglas, "A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity", IEEE Transactions on Neural Networks, vol. 17, no. 1 pp. 211-221, 2006.
[CrossRef] [Web of Science Times Cited 404] [SCOPUS Times Cited 435]


[13] M. J. Pearson, A. G. Pipe, B. Mitchinson, K. Gurney, C. Melhuish, I. Gilhespy, M. Nibouche, "Implementing Spiking Neural Networks for Real Time Signal Processing and Control Applications: A Model Validated FPGA Approach", IEEE Transactions on Neural Networks, vol. 18, no. 5, pp. 1472-1487, 2007.
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 69]


[14] P. Arena, L. Fortuna, M. Frasca, L. Patane, "Learning Anticipation via Spiking Networks: Application to Navigation Control", IEEE Transactions on Neural Networks, vol. 20, no. 2, pp. 202-216, 2009.
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 46]


[15] H. Markram, J. Lubke, M. Frotscher, B. Sakmann, "Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs", Science, vol. 275, pp. 213-215, 1997.
[CrossRef] [Web of Science Times Cited 2089] [SCOPUS Times Cited 2228]


[16] D. O. Hebb, "The organization of behavior", New York: Wiley & Sons, 1949

[17] P. P. Chu, "RTL Hardware Design Using VHDL. Coding for Efficiency, Portability and Scalabilty", Wiley and Sons, 2006.

[18] "Cyclone II Device Handbook", [Online] Available: Temporary on-line reference link removed - see the PDF document

[19] "Quartus II Handbook", [Online] Available: Temporary on-line reference link removed - see the PDF document

[20] A. Rosado-Munoz, A.B. Fijalkowski, M. Bataller-Mompean, J. Guerrero-Martinez, "FPGA implementation of Spiking Neural Networks supported by a Software Design Environment", Proccedings of the 18th IFAC World Congress, 2011.
[CrossRef] [SCOPUS Times Cited 6]


[21] "Spartan 3E FPGA device family: data sheet", [Online] Available: Temporary on-line reference link removed - see the PDF document

[22] J. A. Bailey, R. Wilcock, P. R. Wilson, J. E. Chad, "Behavioral simulation and synthesis of biological neuron systems using synthesizable VHDL", Neurocomputing, vol. 74, pp. 2392-2406, 2011.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 6]


[23] E. Ros, E. M. Ortigosa, R. Agis, R. Carrillo, M. Arnold, "Real-time computing platform for spiking neurons (RT-spike)", IEEE Transactions on Neural Networks, vol. 17, no.4, pp. 1050-1063, 2006.
[CrossRef] [Web of Science Times Cited 51] [SCOPUS Times Cited 56]


[24] "Vitex 2 FPGA device family: complete data sheet", [Online] Available: Temporary on-line reference link removed - see the PDF document

References Weight

Web of Science® Citations for all references: 3,949 TCR
SCOPUS® Citations for all references: 4,404 TCR

Web of Science® Average Citations per reference: 165 ACR
SCOPUS® Average Citations per reference: 184 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-07-22 08:02 in 62 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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