Behavioral simulation and synthesis of biological neuron systems using synthesizable VHDL

Neurocomputing

Volumn 74, Issue 14-15, 2011, Pages 2392-2406

  Bailey, J A ^a   Wilcock, R ^a   Wilson, P R ^a   Chad, J E ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Hardware; Network; Neuron; Simulation; VHDL

Indexed keywords

BEHAVIORAL SIMULATION; BIOLOGICAL ENTITIES; BIOLOGICAL NEURON SYSTEMS; CAENORHABDITIS ELEGANS; COMPLEX MODEL; COMPUTER MODELS; FPGA-BASED IMPLEMENTATION; LOCOMOTION SYSTEM; MEASURED RESULTS; MODELSIM; NERVOUS SYSTEM; NETWORK; NEURON MODEL; NEURON SYSTEMS; REAL TIME; SIMULATION; SIMULATION TIME; VHDL;

BIOLOGICAL SYSTEMS; COMPUTER HARDWARE; COMPUTER HARDWARE DESCRIPTION LANGUAGES; COMPUTER SIMULATION; FIELD PROGRAMMABLE GATE ARRAYS (FPGA);

REAL TIME SYSTEMS;

ARTICLE; CAENORHABDITIS ELEGANS; CELL FUNCTION; CELL KINETICS; COMPUTER MODEL; CONTROLLED STUDY; INTERMETHOD COMPARISON; LOCOMOTION; MOTONEURON; NERVE CELL; NONHUMAN; PRIORITY JOURNAL; SYNAPTIC TRANSMISSION;

EID: 79957945206     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.04.001     Document Type: Article

References (38)

1. S. Modi, P. Wilson, A. Brown, J. Chad, Behavioral simulation of biological neuron systems in systems, in: IEEE Behavioral Modeling and Simulation Conference Proceedings, 2004, pp. 31-36.
2. J. Bailey, P. Wilson, A. Brown, J. Chad, Behavioral simulation of biological neuron systems using VHDL and VHDL-AMS, in: IEEE Behavioral Modeling and Simulation Conference Proceedings, 2007, pp. 153-158.
3. J. Bailey, P. Wilson, A. Brown, J. Chad, Behavioral simulation of biological neuron systems using VHDL and VHDL-AMS, in: IEEE Behavioral Modeling and Simulation Conference Proceedings, 2007, pp. 153-158.
4. Brette R., Rudolph M., Carnevale T., Hines M., Beeman D., Bower J., Diesmann M., Morrison A., Goodman P., Harris F., Zirpe M., Natschläger T., Pecevski D., Ermentrout B., Djurfeldt M., Lansner A., Rochel O., Vieville T., Muller E., Davison A., ElBoustani S., Destexhe A. Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 2007, 23:349-398.
5. Yamada W.M., Zucker R.S. Time course of transmitter release calculated from simulations of a calcium diffusion model. Biophys. J. 1992, 61:671-682.
6. McCulloch W.S., Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol. 1943, V5:115-133.
7. Hodgkin A.L., Huxley A.F. A quantitative description of ion currents and its applications to conduction and excitation in nerve membranes. J. Physiol. 1952, 117:500-544.
8. A. Destexhe, Z. Mainen, T. Sejnowski, Kinetic models of synaptic transmission, Methods in Neuronal Modelling: From Ions to Networks, 1998, pp. 1-26.
9. W. Rall, H. Agmon-Snir, Cable theory for dendritic neurons, in: Methods in Neuronal Modeling: From Ions to Networks, 1998, pp. 27-92.
10. Bower M., Beeman D. The Book of Genesis 1998, Springer-Verlag.
11. Hines M.L., Carnevale N.T. The neuron simulation environment. Neural Comput. 1997, 9:1179-1209.
12. Lapique L. Recherches quantitatives sur l'excitation électrique des nerfs traitée comme une polarisation. J. Physiol. (Paris) 1907, 9:620-635.
13. F. Gabbiani, C. Koch, Principles of spike train analysis, in: Methods in Neuronal Modeling: From Ions to Networks, 1998, pp. 314-360.
14. Poggio T., Torre V. A Volterra representation for some neuron models. Biol. Cybern. 1977, 27.
15. Calvin W.H., Stevens C.F. Synaptic noise and other sources of randomness in motoneuron interspike intervals. J. Neurophysiol. 1968, 31:574-587.
16. Softky W.R., Koch C. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 1993, 13:334-350.
17. Segev I. Single neurone models: oversimple, complex and reduced. Trends Neurosci. 1992, 15:414-421.
18. Izhikevich E. Simple model of spiking neurons. IEEE Trans. Neural Networks 2003, 14:1569-1572.
19. Claverol E.T., Cannon R.C., Chad J.E., Brown A.D. Event based neuron models for biological simulation. A model of the locomotion circuitry of the nemotode C. elegans. Computational Intelligence and Applications 1999, World Scientific Engineering Society Press.
20. Pytte E., Grinstein G., Traub R. Cellular automaton models of the CA3 region of the hippocampus. Network: Computation in Neural Systems 1991, 2:149-167.
21. E.T. Claverol, An event-driven approach to biologically realistic simulation of neural aggregates, Ph.D. Thesis, University of Southampton, UK, 2000.
22. Claverol E.T., Brown A.D., Chad J.E. Discrete simulation of large aggregates of neurons. Neurocomputing 2002, 47:277-297.
23. D.C. Lopes, R.M. Magalhaes, J.D. Melo, A.D.D. Neto, Implementation and evaluation of modular neural networks in a multiple processor system on chip to classify electric disturbance, in: International Conference on Computational Science and Engineering 2009 CSE '09, vol. 1, 2009, pp. 412-417.
24. S. Furber, A. Brown, Biologically-inspired massively-parallel architectures-computing beyond a million processors, in: Ninth International Conference on Application of Concurrency to System Design, 2009. ACSD '09, 2009, pp. 3-12.
25. White J.G., Southgate E., Thomson J.N., Brenner S. Structure of ventral nerve cord of Caenorhabditis elegans. Philos. Trans. R. Soc. London Ser. B Biol. Sci. 1976, 275:327-348.
26. Bono M.d., Maricq A.V. Neuronal substrates of complex behaviors in C. elegans. Annu. Rev. Neurosci. 2005, 28:451-501.
27. White J.G., Southgate E., Thomson J.N., Brenner S. The structure of the nervous system of the nematode Caenorhabditis elegans. Philos. Trans. R. Soc. London 1986, 314:1-340.
28. Braitenberg V., Atwood R. Morphological observations on the cerebellar cortex. J. Comput. Neurol. 1958, 109:34.
29. Eccles J., Ito M., Szentagothai J. The Cerebellum as a Neuronal Machine 1967, Springer-Verlag, Berlin.
30. Stretton A., Fishpool R., Southgate E., Donmoyer J., Walrond J., Moses J., Kass I. Structure and physiological activity of the motorneurons of the nematode ascaris. Proc. Natl. Acad. Sci. 1978, 75:3493-3497.
31. Wicks S.R., Rankin C.H. The integration of antagonistic reflexes revealed by laser ablation of identified neurons determines habituation kinetics of the Caenorhabditis elegans tap withdrawal response. J. Comput. Physiol. [A] 1996, 179:675-685.
32. Sulston J., Horvitz H. Post-embryonic lineages of the nematode, Caenorhabditis elegans. Dev. Biol. 1977, 56:110-156.
33. Chalfie M., Sulston J., White J., Southgate E., Thomson J., Brenner S. The neural circuit for touch sensitivity in Caenorhabditis elegans. J. Neurosci. 1985, 5:956-964.
34. C.D. Johnson, A.O.W. Stretton, Neural control of locomotion in ascaris: anatomya electrophysiology and biochemistry, in: Nematodes as Biological Models, 1980, pp. 159-195.
35. Niebur E., Erdos P. Theory of the locomotion of nematodes: control of the somatic motor neurons by interneurons. Math. Biosci. 1993, 118:51-82.
36. Von Stetina S.E., Treinin M., Miller D.M. The motor circuit. Neurobiol. C. Elegans 2006, 69:125-167.
37. Jin Y., Jorgensen E., Hartwieg E., Horvitz H.R. The Caenorhabditis elegans gene UNC-25 encodes glutamic acid decarboxylase and is required for synaptic transmission but not synaptic development. J. Neurosci. 1999, 19:539-548.
38. Claverol E.T., Brown A.D., Chad J.E. A large-scale simulation of the piriform cortex by a cell automaton-based network model. IEEE Trans. Biomed. Eng. 2002, 49:921-934.