Analog VLSI implementation of resonate-and-fire neuron

International Journal of Neural Systems

Volumn 16, Issue 6, 2006, Pages 445-456

  Nakada, Kazuki ^a   Asai, Tetsuya ^b   Hayashi, Hatsuo ^a  

^a KYUSHU INSTITUTE OF TECHNOLOGY   (Japan)

^b HOKKAIDO UNIVERSITY   (Japan)

Author keywords

Analog integrated circuit; Coincidence detection; Frequency preference; Resonate and fire neuron (RFN) model; Spiking neural networks; Very large scale integration (VLSI)

Indexed keywords

ACTION POTENTIAL; ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BIOLOGICAL RHYTHM; NERVE CELL; NERVE CELL NETWORK; PHYSIOLOGY;

ACTION POTENTIALS; ANIMALS; BIOLOGICAL CLOCKS; MODELS, NEUROLOGICAL; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURONS;

EID: 33846673095     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065706000846     Document Type: Article

References (53)

1. C. A. Mead, Analog VLSI and Neural Systems (Addison-Wesley, Reading, 1989).
2. S.-C. Liu, J. Kramer, G. Indiveri, T. Delbruck and R. Douglas, Analog VLSI: Circuits and Principles (MIT Press, 2002).
3. W. Maass and C. M. Bishop (ed.), Pulsed Neural Networks (MIT Press, 1998).
4. T. Fukai, Competition in the temporal domain among neural activities phase-locked to subthreshold oscillations, Biol. Cybern. 75 (1996) 453-461.
5. M. Hanagata, Y. Horio and K. Aihara, Asynchronous pulse neural network model for VLSI implementation, IEICE Trans. Fund. E81-A(9) (1998) 1853-1859.
6. W. Senn, I. Segev and M. Tsodyks, Reading neuronal synchrony with depressing synapses, Neural Computation 10(4) (1998) 815-819.
7. D. J. Mar, C. C. Chow, W. Gerstner, R. W. Adams and J. J. Collins, Noise shaping in populations of coupled model neurons, in Proc. Natl. Acad. Sci. 96(18) (1999) 10450-10455.
8. F. Wörgötter, A. Cozzi and V. Gerdes, A parallel noise-robust algorithm to recover depth information from radial flow fields, Neural Computation 11 (1999) 381-416.
9. S. Fusi and M. Mattia, Collective behavior of networks with linear (VLSI) Integrate and Fire Neurons, Neural Computation 11 (1999) 633-652.
10. S. Fusi, M. Annunziato, D. Badoni, A. Salamon and D. J. Amit, Spike-driven synaptic plasticity: Theory, simulation, VLSI implementation, Neural Computation 12 (2000) 2227-2258.
11. T. Fukai and S. Kanemura, Noise-tolerant stimulus discrimination by synchronization with depressing synapses, Biol. Cybern. 85 (2001) 107-116.
12. G. Bugmann, Synaptic depression increases the selectivity of a neuron to its preferred pattern and binarizes the neural code, Biosystems 67 (2002) 17-26.
13. R. Reeve and B. Webb, New neural networks for robot phonotaxis, Philos. Trans. A Math. Phys. Eng. Sci. 361 (2003) 2245-2266.
14. J. J. Hopfield and C. D. Brody, Learning rules and network repair in spike-timing-based computational networks, in Proc. Nacl. Acad. Sci. 101(1) (2004) 337-342.
15. M. Mahowald, An Analog VLSI System for Stereoscopic Vision (Boston, MA: Kluwer Academic Publishers, 1994).
16. F. Tenore, R. Etienne-Cummings and M. A. Lewis, A programmable array of silicon neurons for the control of legged locomotion, in Proc. IEEE Int. Symp. Circ. Syst. (2003).
17. D. H. Goldberg, G. Cauwenberghs and A. G. Andreou, Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons, Neural Networks 14(6-7) (2001) 781-793.
18. S.-C. Liu, J. Kramer, G. Indiveri, T. Delbruck, T. Burg and R. J. Douglas, Orientation-selective aVLSI spiking neurons, Neural Networks 14(6-7) (2001) 629-643.
19. E. Culurciello, R. Etienne-Cummings and K. Boahen, A biomorphic digital image sensor, IEEE Journal of Solid-State Circuits 8(2) (2003) 281-294.
20. D. C. Ng, K. Isakari, A. Uehara, K. Kagawa, T. Tokuda, J. Ohta and M. Nunoshita, A study of bending effect on pulsed frequency modulation based photosensor for retinal prosthesis, Jpn. J. Appl. Phys. 42(12) (2003) 7621-7624.
21. T. Y. W. Choi, B. E. Shi and K. A. Boahen, An ON-OFF orientation selective address event representation image transceiver chip, IEEE Trans. Circ. Sys.-I 51(2) (2004) 342-353.
22. R. J. Vogelstein, U. Mallik, G. Cauwenberghs, E. Culurciello and R. Etienne-Cummings, Saliency-driven image acuity modulation on a reconfigurable silicon array of spiking neurons, Adv. Neural Info. Proc. Syst. 16 (2005).
23. A. Linares-Barranco, G. Jimenez-Moreno, B. Linares-Barranco and A. Civit-Ballcels, On algorithmic rate-coded AER generation, IEEE Trans. on Neural Networks in printing.
24. N. Kumer, W. Himmelbauer, G. Cauwenbeghs and A. Andreou, An analog VLSI chip with asynchronous interface for auditory feature extraction, IEEE Trans. Circ. Sys.-II 45(5) (1998) 600-606.
25. M. Cheely and T. Horiuchi, Analog VLSI models of range-tuned neurons in the bat echolocation system, EURASIP J. Appl. Signal Proc. 7 (2003) 649-658.
26. R. Reeve, B. Webb, A. Horchler, G. Indiveri and R. Quinn, New technologies for testing a model of cricket phonotaxis on an outdoor robot platform, Robotics and Autonomous Systems 51(1) (2005) 41-54.
27. A. Van Schaik and S.-C. Liu, AER EAR: A matched silicon cochlea, pair with address event representation interface, in Proc. IEEE Int. Symp. on Circ. and Syst. Kobe, Japan, May V (2005) 4213-4216.
28. S.-C. Liu and R. J. Douglas, Temporal coding in a network of silicon integrate-and-fire neurons, IEEE Trans. Neural Networks 15(5) (2004) 1305-1314.
29. Y. Kanazawa, T. Asai, M. Ikebe and Y. Amemiya, A novel CMOS circuit for depressing synapse and its application to contrast-invariant pattern classification and synchrony detection, Int. J. Robotics and Automation 19(4) (2004) 206-212.
30. H. Tanaka, T. Morie and K. Aihara, Associative memory operation in a Hopfield-type spiking neural network with modulation of resting membrane potential, presented at Int. Symp. on Nonlinear Theory and its Applications, Bruges, Belgium (2005).
31. G. Indiveri, E. Chicca and R. J. Douglas, A VLSI array of low-power spiking neurons and bistable synapse with spike-timing dependent plasticity, IEEE Trans. Neural Networks, to be appeared.
32. D. Muir, G. Indiveri and R. Douglas, Form specifies function: Robust spike-based computation in analog VLSI without precise synaptic weights, in Proc. the IEEE Int. Sympo. Circ. and Syst., Kobe, Japan (May 2005).
33. A. Utagawa, T. Asai, T. Hirose and Y. Amemiya, A neuromorphic LSI performing noise-shaping pulse-density modulation with ultralow-power subthreshold neuron circuits, 10th International Conference on Cognitive and Neural Systems, Boston, USA (May 2006).
34. J. V. Arthur and K. Boahen, Learning in silicon: Timing is everything, Adv. Neural Info. Proc. Syst. 17 (2006).
35. C. Rasche and R. H. R. Hahnloser, Silicon synaptic depression, Biol. Cybern. 84 (2001) 57-62.
36. S.-C. Liu, Analog VLSI circuits for short-term dynamic synapses, EURASIP J. Appl. Signal Proc. 7 (2003) 620-628.
37. R. Z. Shi and T. Horiuchi, A summating, exponentially-decaying CMOS synapse for spiking neural systems, Adv. Neural Info. Proc. Syst. 16 (2004).
38. R. J. Vogelstein, F. Tenore, R. Philip, M. S. Adlerstein, D. H. Goldberg and G. Cauwenberghs, Spike timing-dependent plasticity in the address domain, Adv. Neural Info. Proc. Syst. 15 (2003).
39. A. Bofill-i-Petit and A. F. Murray, Synchrony detection and amplification by silicon neurons with STDP synapses, IEEE Trans. Neural Networks 15(5) (2004) 1296-1304.
40. K. Cameron, V. Boonsobhak, A. Murray and D. Ranshaw, Spike timing dependent plasticity (STDP) can ameliorate process variations in neuromorphic. systems, IEEE Trans. Neural Networks 16(6) (2005) 1626-1637.
41. G. M. Tovar, T. Hirose, T. Asai and Y. Amemiya, Precisely-timed synchronization among spiking neural circuits on analog VLSIs, in Proc. the 2006 RISP International Workshop on Nonlinear Circuits amd Signal Processing Honolulu, USA (March 2006), pp. 62-65.
42. S. R. Schultz and M. A. Jabri, Analogue VLSI integrate-and-fire neuron with frequency adaptation, Electronic Letters 31(16) (1995) 1357-1358.
43. K. A. Boahen, Retinomorphic Vision Systems: Reverse Engineering the Vertebrate Retina, Ph.D. Thesis, California Institute of Technology, Pasadena CA (1997).
44. A. Van Schaik, Building blocks for electronic spiking neural networks, Neural Networks 14(6-7) (2001) 617-628.
45. S.-C. Liu and B. A. Minch, Homeostasis in a silicon integrate-and-fire neuron, Adv. Neural Info. Proc. Syst. 13 (2001).
46. T. Asai, Y. Kanazawa and Y. Amemiya, A subthreshold MOS neuron circuit based on the Volterra system, IEEE Trans. Neural Networks 14(5) (2003) 1308-1312.
47. H. Nakano and T. Saito, Grouping synchronization in a pulse-coupled network of chaotic spiking oscillators, IEEE Trans. Neural Networks 15(5) (2004) 1018-1026.
48. Y. Horio, T. Taniguchi and K. Aihara, An asynchronous spiking chaotic neuron integrated circuit, Neurocomputing 64 (2005) 447-472.
49. K. Nakada, T. Asai and H. Hayashi, A silicon resonate-and-fire neuron based on the Volterra system, in Proc. the 2005 International Symposium on Nonlinear Theory and Its Applications, 82-85, Bruges, Belgium (2005).
50. E. M. Izhikevich, Resonate-and-fire neurons, Neural Networks 14 (2001) 883-894.
51. E. M. Izhikevich, Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (The MIT press, 2006).
52. M. J. M. Pelgrom, A. C. J. Duinmaijer and A. P. G. Welbers, Matching properties of MOS transistors, IEEE Journal of Soilid-State Circuits SC-24 (1989) 1433-1440.
53. S. N. Goel, C. S. Maitra and W. E. Montroll, On the Volterra and other nonlinear models of interacting populations, Rev. Mod. Phys. 43 (1971) 231-276.