A hardware-oriented learning algorithm for a digital spiking neuron

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2008, Pages 2472-2479

  Torikai, Hiroyuki ^a   Hashimoto, Sho ^a  

^a OSAKA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER HARDWARE DESCRIPTION LANGUAGES; DIGITAL ARITHMETIC; EDUCATION; ELECTRIC WIRING; FIELD PROGRAMMABLE GATE ARRAYS (FPGA); LEARNING SYSTEMS; LOCOMOTIVES; NEURAL NETWORKS; RAILROAD CARS; SHIFT REGISTERS; TELEPHONE SYSTEMS;

ANALYSIS RESULTS; EXPERIMENTAL MEASUREMENTS; HARDWARE DESCRIPTION LANGUAGES; NEURON MODELS; SPIKING NEURON MODELS; SPIKING NEURONS; WIRING PATTERNS;

LEARNING ALGORITHMS;

EID: 56349099311     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2008.4634143     Document Type: Conference Paper

References (18)

1. R. Perez and L. Glass, Bistability, period doubling bifurcations and chaos in a periodically forced oscillator, Phys. Lett. vol.90A, no.9, pp.441-443, 1982.
2. H. Torikai and T. Saito, Resonance phenomenon of interspike intervals from an Integrate-and-Fire Model with two periodic inputs, IEEE Trans. CAS-I, vol.48, no.10, pp.1198-1204, 2001.
3. H. Hamanaka, H. Torikai and T. Saito, Quantized spiking neuron with A/D conversion functions, IEEE Trans. CAS-II, vol.53, no. 10, pp. 1049-1053, 2006.
4. E.M. Izhikevich, Dynamical systems in neuroscience, MIT Press, 2006.
5. B. Lindner, M.J. Chacron and A. Longtin, Integrate-and-fire neurons with threshold noise: A tractable model of how interspike interval correlations affect neuronal signal transmission, Physical Review E 72, 021911, 2005.
6. H. Nakano and T. Saito, Grouping synchronization in a pulse-coupled network of chaotic spiking oscillators, IEEE Trans. Neural Networks, 15, 5, pp. 1018-1026, 2004.
7. H. Torikai and T. Saito, Synchronization phenomena in pulse-coupled networks driven by spike-train inputs, IEEE Trans. Neural Netwotks, vol. 15, no. 2, pp.337-347, 2004.
8. G. Lee and N. H. Farhat, The bifurcating neuron network 2, Neural networks, vol.15, pp.69-84, 2002.
9. J. J. Hopfield and A. V. M. Herz, Rapid local synchronization of action potentials: Toward computation with coupled integrate-and-fire neurons, Proc. Natl. Acad. Sci. USA, vol. 92, pp. 6655-6662, 1995.
10. S. R. Campbell, D. Wang and C. Jayaprakash, Synchrony and desynchrony in integrate-and-fire oscillators, Neural computation, vol. 11, pp. 1595-1619, 1999.
11. R. Eckhorn, Neural mechanisms of scene segmentation: recordings from the visual cortex suggest basic circuits for linking field models, IEEE Trans. Neural Networks, vol.10, no.3, pp.464-479, 1999.
12. H. Torikai, H. Hamanaka and T. Saito, Reconfigurable Digital Spiking Neuron and its Pulse-Coupled Network: Basic Characteristics and Potential Applications, IEEE Trans. CAS-II, vol.53, no.8, pp. 734-738, 2006.
13. H. Torikai, Basic Characteristics and Learning Potential of a Digital Spiking Neuron, IEICE Trans. Fund., Vol.E90-A No. 10, pp.2093-2100, 2007.
14. H. Torikai, A. Funew and T. Saito, Digital spiking neuron and its learning for approximation of various spike-trains, Neural Netwotks, 2008 (to appear).
15. FPGA and HDL software package can be obtained from http://www.xilinx.com/
16. S. Martignoli, J.-J. van der Vyver, A. Kern, Y. Uwate and R. Stoop, Analog electronic cochlea with mammalian hearing characteristics, Applied Physics Letters, 91, 064108, 2007.
17. G. Bi and M. Poo, Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type, J. Neurosci., vol. 18, pp. 10464-10472, 1998.
18. S. Iplikci, Dynamic Reconstruction of Chaotic Systems from Inter-spike Intervals Using Least Squares Support Vector Machines, Physica D, Vol. 216, pp. 282-293, 2006.