Reducing spike train variability: A computational theory of spike-timing dependent plasticity

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages

  Bohte, Sander M ^a,b   Mozer, Michael C ^b  

^a CWI   (Netherlands)

^b UCB 450   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTROPHYSIOLOGY;

COMPUTATIONAL THEORIES; OBJECTIVE FUNCTIONS; POST-SYNAPTIC NEURONS; PRE-SYNAPTIC NEURON; SPIKE-TIMING-DEPENDENT PLASTICITY; SYNAPTIC MODULATION; SYNAPTIC PLASTICITY; SYNAPTIC POTENTIATION;

NEURONS;

EID: 84898973877     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. G-q. Bi and M-m. Poo. Synaptic modification by correlated activity: Hebb's postulate revisited. Ann. Rev. Neurosci., 24:139-166, 2001.
2. A. Kepecs, M.C.W. van Rossum, S. Song, and J. Tegner. Spike-timing-dependent plasticity: common themes and divergent vistas. Biol. Cybern., 87:446-458, 2002.
3. A. Saudargiene, B. Porr, and F. Worgotter. How the shape of pre- And postsynaptic signals can influence stdp: A biophysical model. Neural Comp., 16:595-625, 2004.
4. W. Gerstner, R. Kempter, J. L. van Hemmen, and H. Wagner. A neural learning rule for sub-millisecond temporal coding. Nature, 383:76-78, 1996.
5. S. Song, K. Miller, and L. Abbott. Competitive hebbian learning through spiketime -dependent synaptic plasticity. Nat. Neurosci., 3:919-926, 2000.
6. R. van Rossum, G.-q. Bi, and G.G. Turrigiano. Stable hebbian learning from spike time dependent plasticity. J. Neurosci., 20:8812-8821, 2000.
7. L.F. Abbott and W. Gerstner. Homeostasis and Learning through STDP. In D. Hansel et al(eds), Methods and Models in Neurophysics, 2004.
8. R.P.N. Rao and T.J. Sejnowski. Spike-timing-dependent plasticity as temporal difference learning. Neural Comp., 13:2221-2237, 2001.
9. P. Dayan and M. Hausser. Plasticity kernels and temporal statistics. In S. Thrun, L. Saul, and B. Scholkopf, editors, NIPS 16. 2004.
10. G. Chechik. Spike-timing-dependent plascticity and relevant mutual information maximization. Neural Comp., 15:1481-1510, 2003.
11. R.C. Froemke and Y. Dan. Spike-timing-dependent synaptic modification induced by natural spike trains. Nature, 416:433-438, 2002.
12. L.l. Zhang, H.W. Tao, C.E. Holt, W.A. Harris, and M-m. Poo. A critical window for cooperation and competition among developing retinotectal synapses. Nature, 395:37-44, 1998.
13. W. Gerstner. A framework for spiking neuron models: The spike response model. In F. Moss & S. Gielen (eds), The Handbook of Biol. Physics, vol 4, pp 469-516, 2001.
14. A.J. Bell and L.C. Parra. Maximizing information yields spike timing dependent plasticity. NIPS 17. 2005.
15. T. Toyoizumi, J-P. Pfister, K. Aihara, and W. Gerstner. Spike-timing dependent plasticity and mutual information maximization for a spiking neuron model. NIPS 17. 2005.
16. R. Jolivet, T.J. Lewis, and W. Gerstner. The spike response model: A framework to predict neuronal spike trains. In Kaynak et al.(eds), Proc. ICANN/ICONIP 2003, pp 846-853. 2003.
17. A. Herrmann and W. Gerstner. Noise and the PSTH response to current transients: I. J. Comp. Neurosci., 11:135-151, 2001.
18. X. Xie and H.S. Seung. Learning in neural networks by reinforcement of irregular spiking. Physical Review E, 69(041909), 2004.