An artificial early visual model adopting spike-timing-dependent plasticity

Neurocomputing

Volumn 69, Issue 16-18, 2006, Pages 1904-1911

  Yang, Zhijun ^a,b   Murray, Alan F ^b  

^a NANJING NORMAL UNIVERSITY   (China)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Depth recovery; Monocular vision model; Spike timing dependent plasticity; Spiking neurons

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; MATHEMATICAL MODELS; PATTERN MATCHING; VLSI CIRCUITS;

MONOCULAR VISION MODEL; SPIKE-TIMING-DEPENDENT PLASTICITY; SPIKING NEURONS;

NEURAL NETWORKS;

ARTICLE; COMPUTER MODEL; IMAGE DISPLAY; INTERNEURON; MONOCULAR VISION; NERVE CELL; NERVE CELL PLASTICITY; PHOTORECEPTOR; PRIORITY JOURNAL; SIMULATION; SPIKE; SYNAPSE;

EID: 33748430180     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2005.11.017     Document Type: Article

References (18)

1. Abbott L.F., and Kepler T.B. Model neurons: from Hodgkin-Huxley to Hopfield. In: Garrido L. (Ed). Statistical Mechanics of Neural Networks (1990), Springer, Berlin
2. Bi G.Q., and Poo M.M. Activity-induced synaptic modifications in hippocampal culture, dependence on spike timing, synaptic strength and cell type. J. Neurosci. 18 (1998) 10464-10472
3. Borisyuk R. Oscillatory activity in the neural networks of spiking elements. BioSystems 67 (2002) 3-16
4. Dayan P., and Abbott L.F. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (2001), MIT Press, Cambridge, MA
5. Engel S.A., Glover G.H., and Wandell B.A. Retinotopic organization in human visual cortex and the spatial precision of functional MRI. Cereb. Cortex 7 (1997) 181-192
6. Gerstner W. Populations of spiking neurons. In: Maass W., and Bishop C.M. (Eds). Pulsed Neural Networks (1999), MIT press, Cambridge, MA
7. Holmes G. The representation of the visual field in human striate cortex. Arch. Ophthalmol. 109 (1991) 816-824
8. Kempter R., Gerstner W., and van Hemmen J.L. Hebbian learning and spiking neurons. Phys. Rev. E 59 (1999) 4498-4514
9. Markram H., Lubke J., Frotscher M., and Sakmann B. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275 (1997) 213-215
10. Perkel D.H. A computer program for simulating a network of interacting neurons. I. Organization and physiological assumptions. Comput. Biomed. Res. 9 (1976) 31-43
11. Roberts P.D., and Bell C.C. Spike timing dependent synaptic plasticity in biological systems. Biol. Cybern. 87 (2002) 392-403
12. Rossum M.V., Bi G.Q., and Turrigiano G.G. Stable Hebbian learning from spike-timing-dependent plasticity. J. Neurosci. 20 23 (2000) 8812-8821
13. Song S., Miller K.D., and Abbott L.F. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neurosci. 3 9 (2000) 919-926
14. Troyer T.W., and Miller K.D. Integrated-and-fire neurons matched to physiological f-I curves yield high input sensitivity and wide dynamic range. In: Bower J.M. (Ed). Computational Neuroscience: Trends in Research (1997), Academic Press, New York
15. Tuckwell H.C. Introduction to Theoretical Neurobiology: Nonlinear and Stochastic Theories (1988), Cambridge University Press, Cambridge
16. Wörgötter F., Cozzi A., and Gerdes V. A parallel noise-robust algorithm to recover depth information from radial flow fields. Neural Comput. 11 (1999) 381-416
17. Yang D., and Poo M.M. Spike timing-dependent plasticity of neural circuits. Neuron 44 (2004) 23-30
18. Zhang L.I., Tao H.W., Holt C.E., Harris W.A., and Poo M.M. A critical window for cooperation and competition among developing retinotectal synapses. Nature 395 (1998) 37-44