Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning

Neural Computation

Volumn 18, Issue 6, 2006, Pages 1318-1348

  Pfister, Jean Pascal ^a   Toyoizumi, Taro ^a   Barber, David ^a   Gerstner, Wulfram ^a  

^a EPFL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ANIMAL; ARTICLE; BIOLOGICAL MODEL; HUMAN; LEARNING; NERVE CELL PLASTICITY; PHYSIOLOGY; STATISTICS; SYNAPSE;

ACTION POTENTIALS; ANIMALS; HUMANS; LEARNING; MODELS, NEUROLOGICAL; NEURONAL PLASTICITY; STOCHASTIC PROCESSES; SYNAPSES;

EID: 33646801243     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2006.18.6.1318     Document Type: Article

References (60)

1. Abarbanel, H., Huerta R., & Rabinovich, M. (2002). Dynamical model of long-term synaptic plasticity. Proc. Natl. Academy of Sci. USA, 59, 10137-10143.
2. Atick, J., & Redlich, A. (1990). Towards a theory of early visual processing. Neural Computation, 4, 559-572.
3. Barber, D. (2003). Learning in spiking neural assemblies. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems, 15 (pp. 149-156). Cambridge, MA: MIT Press.
4. Barlow, H. B. (1961). Possible principles underlying the transformation of sensory messages. In W. A. Rosenblith (Ed.), Sensory communication (pp. 217-234). Cambridge, MA: MIT Press.
5. Bell, A., & Sejnowski, T. (1995). An information maximization approach to blind separation and blind deconvolution. Neural Computation, 7, 1129-1159.
6. Bell, A. J., & Parra, L. C. (2005). Maximising sensitivity in a spiking network. In L. K. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in neural information processing systems, 17 (pp. 121-128). Cambridge, MA: MIT Press.
7. Bell, C., Han, V., Sugawara, Y., & Grant, K. (1997). Synaptic plasticity in a cerebellum-like structure depends on temporal order. Nature, 387, 278-281.
8. Bi, G., & Poo, M. (1998). Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci., 18, 10464-10472.
9. Bi, G., & Poo, M. (1999). Distributed synaptic modification in neural networks induced by patterned stimulation. Nature., 401, 792-796.
10. Bi, G., & Poo, M. (2001). Synaptic modification of correlated activity: Hebb's postulate revisited. Ann. Rev. Neurosci., 24, 139-166.
11. Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Clarendon Press.
12. Bohte, S. M., & Mozer, M. C. (2005). Reducing spike train variability: A computational theory of spike-timing dependent plasticity. In L. K. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in neural information processing systems, 17 (pp. 201-208). Cambridge, MA: MIT Press.
13. Brody, C., & Hopfield, J. (2003). Simple networks for spike-timing-based computation, with application to olfactory processing. Neuron, 37, 843-852.
14. Bugmann, G., Christodoulou, C., & Taylor, J. G. (1997). Role of temporal integration and fluctuation detection in the highly irregular firing of leaky integrator neuron model with partial reset. Neural Computation, 9, 985-1000.
15. Carr, C. E., & Konishi, M. (1990). A circuit for detection of interaural time differences in the brain stem of the barn owl. J. Neurosci., 10, 3227-3246.
16. Chechik, G. (2003). Spike-timing-dependent plasticity and relevant mututal information maximization. Neural Computation, 15, 1481-1510.
17. Dayan, P., & Häusser, M. (2004). Plasticity kernels and temporal statistics. In S. Thrun, L. K. Saul, & B. Schölkopf (Eds.), Advances in neural information processing systems, 16. Cambridge, MA: MIT Press.
18. Feldman, D. (2000). Timing-based LTP and LTD and vertical inputs to layer II/III pyramidal cells in rat barrel cortex. Neuron, 27, 45-56.
19. Frégnac, Y., Shulz, D. E., Thorpe, S., & Bienenstock, E. (1988). A cellular analogue of visual cortical plasticity. Nature, 333(6171), 367-370.
20. Frégnac, Y., Shulz, D. E., Thorpe, S., & Bienenstock, E. (1992). Cellular analogs of visual cortical epigenesis. I: Plasticity of orientation selectivity, Journal of Neuroscience, 12(4), 1280-1300.
21. Gerstner, W. (2001). Coding properties of spiking neurons: Reverse- and cross-correlations. Neural Networks, 14, 599-610.
22. Gerstner, W., Kempter, R., van Hemmen, J. L., & Wagner, H. (1996). A neuronal learning rule for sub-millisecond temporal coding. Nature, 383, 76-78.
23. Gerstner, W., & Kistler, W. K. (2002a). Mathematical formulations of Hebbian learning. Biological Cybernetics, 87, 404-415.
24. Gerstner, W., & Kistler, W. K. (2002b). Spiking neuron models. Cambridge: Cambridge University Press.
25. Gerstner, W., Ritz, R., & van Hemmen, J. L. (1993). Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns. Biol. Cybern., 69, 503-515.
26. Gütig, R., Aharonov, R., Rotter, S., & Sompolinsky, H. (2003). Learning input correlations through non-linear temporally asymmetry Hebbian plasticity. J. Neuroscience, 23, 3697-3714.
27. Haykin, S. (1994). Neural networks. Upper Saddle River, NJ: Prentice Hall.
28. Hopfield, J. J. (1995). Pattern recognition computation using action potential timing for stimulus representation. Nature, 376, 33-36.
29. Hopfield, J. J., & Brody, C. D. (2004). Learning rules and network repair in spike-timing-based computation networks. Proc. Natl. Acad. Sci. USA, 101, 337-342.
30. Izhikevich, E. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14, 1569-1572.
31. Johansson, R., & Birznieks, I. (2004). First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nature Neuroscience, 7, 170-177.
32. Karmarkar, U., & Buonomano, D. (2002). A model of spike-timing dependent plasticity: One or two coincidence detectors. J. Neurophysiology, 88, 507-513.
33. Kempter, R., Gerstner, W., & van Hemmen, J. L. (1999). Hebbian learning and spiking neurons. Phys. Rev. E, 59, 4498-4514.
34. Kempter, R., Gerstner, W., & van Hemmen, J. L. (2001). Intrinsic stabilization of outputrates by spike-based Hebbian learning. Neural Computation, 13, 2709-2741.
35. Kistler, W. M., & van Hemmen, J. L. (2000). Modeling synaptic plasticity in conjunction with the timing of pre- and postsynaptic potentials. Neural Comput., 12, 385-405.
36. Legenstein, R., Naeger, C., & Maass, W. (2005). What target functions can be learnt with spike-timing-dependent plasticity? Neural Computation, 17, 2337-2382.
37. Magee, J. C., & Johnston, D. (1997). A synaptically controlled associative signal for Hebbian plastiticy in hippocampal neurons. Science, 275, 209-213.
38. Markram, H., Lübke, J., Frotscher, M., & Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postysnaptic AP and EPSP. Science, 275, 213-215.
39. Mehta, M. R., Lee, A. K., & Wilson, M. A. (2002). Role of experience of oscillations in transforming a rate code into a temporal code. Nature, 417, 741-746.
40. Minsky, M. L., & Papert, S. A. (1969). Perceptrons. Cambridge MA: MIT Press.
41. Panzeri, S., Peterson, R., Schultz, S., Lebedev, & Diamond, M. (2001). The role of spike timing in the coding of stimulus location in rat somatosensory cortex. Neuron, 29, 769-777.
42. Poliakov, A. V., Powers, R. K., & Binder, M. C. (1997). Functional identification of the input-output transforms of motoneurones in the rat and cat. J. Physiology, 504, 401-424.
43. Rao, R. P. N., & Sejnowski, T. J. (2001). Spike-timing-dependent Hebbian plasticity as temporal difference learning. Neural Computation, 13, 2221-2237.
44. Roberts, P. (1999). Computational consequences of temporally asymmetric learning rules: I. Differential Hebbian learning. J. Computational Neuroscience, 7, 235-246.
45. Roberts, P., & Bell, C. (2000). Computational consequences of temporally asymmetric learning rules: II. Sensory image cancellation. Computational Neuroscience, 9, 67-83.
46. Rubin, J., Lee, D. D., & Sompolinsky, H. (2001). Equilibrium properties of temporally asymmetric Hebbian plasticity. Physical Review Letters, 86, 364-367.
47. Senn, W., Tsodyks, M., & Markram, H. (2001). An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing. Neural Computation, 13, 35-67.
48. Seung, S. (2003). Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron, 40, 1063-1073.
49. Shouval, H. Z., Bear, M. F., & Cooper, L. N. (2002). A unified model of NMDA receptor dependent bidirectional synaptic plasticity. Proc. Natl. Acad. Sci. USA, 99, 10831-10836.
50. Sjöström, P., Turrigiano, C., & Nelson, S. (2001). Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron, 32, 1149-1164.
51. Song, S., & Abbott, L. (2001). Column and map development and cortical re-mapping through spike-timing dependent plasticity. Neuron, 32, 339-350.
52. Song, S., Miller, K., & Abbott, L. (2000). Competitive Hebbian learning through spike-time-dependent synaptic plasticity. Nature Neuroscience, 3, 919-926.
53. Thorpe, S., Delorme, A., & Van Rullen, R. (2001). Spike-based strategies for rapid processing. Neural Networks, 14, 715-725.
54. Toyoizumi, T., Pfister, J.-P., Aihara, K., & Gerstner, W. (2005a). Spike-timing dependent plasticity and mutual information maximization for a spiking neuron model. In L. K. Saul, Y. Weiss, and L. Bottou (Eds.), Advances in neural information processing systems, 17 (pp. 1409-1416). Cambridge, MA: MIT Press.
55. Toyoizumi, T., Pfister, J.-P., Aihara, K., & Cerstner, W. (2005b). Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission. Proc. National Academy Sciences (USA), 102, 5239-5244.
56. Troyer, T. W., & Miller, K. (1997). Physiological gain leads to high ISI variability in a simple model of a cortical regular spiking cell. Neural Computation, 9, 971-983.
57. Turrigiano, G., & Nelson, S. (2004). Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience, 5, 97-107.
58. van Rossum, M. C. W., Bi, G. Q., & Turrigiano, G. G. (2000). Stable Hebbian learning from spike timing-dependent plasticity. J. Neuroscience, 20, 8812-8821.
59. Xie, X., & Seung, S. (2004). Learning in neural networks by reinforcement of irregular spiking. Phys. Rev. E, 69, 041909.
60. Zhang, L., Tao, H., Holt, C., Harris, W. A., & Poo, M.-M. (1998). A critical window for cooperation and competition among developing retinotectal synapses. Nature, 395, 37-44.