Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission

Proceedings of the National Academy of Sciences of the United States of America

Volumn 102, Issue 14, 2005, Pages 5239-5244

  Toyoizumi, Taro ^a,b   Pfister, Jean Pascal ^a   Aihara, Kazuyuki ^b,c   Gerstner, Wulfram ^a  

^a EPFL   (Switzerland)

^b UNIVERSITY OF TOKYO   (Japan)

^c JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

Author keywords

Computational neuroscience; Information theory; Learning; Spiking neuron model; Synaptic plasticity

Indexed keywords

ARTICLE; BIENENSTOCK COOPER MUNRO RULE; CALCULATION; CORRELATION ANALYSIS; INFORMATION PROCESSING; LEARNING; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MEMBRANE POTENTIAL; NERVE CELL; NERVE CELL PLASTICITY; NEUROTRANSMISSION; PRESYNAPTIC NERVE; PRIORITY JOURNAL; STATISTICAL ANALYSIS; SYNAPSE; THEORY;

ACTION POTENTIALS; HOMEOSTASIS; MODELS, NEUROLOGICAL; NEURONAL PLASTICITY; NEURONS; SYNAPTIC TRANSMISSION;

EID: 17044416469     PISSN: 00278424     EISSN: None     Source Type: Journal    
DOI: 10.1073/pnas.0500495102     Document Type: Article

References (35)

1. Bliss, T. V. P. & Gardner-Medwin, A. R. (1973) J. Physiol. 232, 357-374.
2. Dudek, S. M. & Bear, M. F. (1993) J. Neurosci. 13, 2910-2918.
3. Artola, A., Bröcher, S. & Singer, W. (1990) Nature 347, 69-72.
4. Levy, W. B. & Stewart, D. (1983) Neuroscience 8, 791-797.
5. Markram, H., Lübke, J., Frotscher, M. & Sakmann, B. (1997) Science 275, 213-215.
6. Bi, G.-Q. & Poo, M.-M. (2001) Annu. Rev. Neurosci. 24, 139-166.
7. Malenka, R. C. & Nicoll, R. A. (1999) Science 285, 1870-1874.
8. Dayan, P. & Abbott, L. F. (2001) Theoretical Neuroscience (MIT Press, Cambridge, MA).
9. Gerstner, W. & Kistler, W. K. (2002) Spiking Neuron Models (Cambridge Univ. Press, Cambridge, U.K.).
10. Cooper, L. N., Intrator, N., Blais, B. S. & Shouval, H. Z. (2004) Theory of Cortical Plasticity (World Scientific, Singapore).
11. Hebb, D. O. (1949) The Organization of Behavior (Wiley, New York).
12. Bienenstock, E. L., Cooper, L. N. & Munro, P. W. (1982) J. Neurosci. 2, 32-48;
13. Artola, A. & Singer, W. (1993) Trends Neurosci. 16, 480-487.
14. Kirkwood, A., Rioult, M. G. & Bear, M. F. (1996) Nature 381, 526-528.
15. Barlow, H. B. & Levick, W. R. (1969) J. Physiol. (London) 200, 1-24.
16. Laughlin, S. (1981) Z. Naturforsch. 36, 910-912.
17. Fairhall, A. L., Lewen, G. D, Bialek, W. & vanSteveninck, R. R. D. (2001) Nature 412, 787-792.
18. Linsker, R. (1989) Neural Comput. 1, 402-411.
19. Nadal, J.-P. & Parga, N. (1997) Neural Comput. 9, 1421-1456.
20. Bell, A. J. & Sejnowski, T. J. (1995) Neural Comput. 7, 1129-1159.
21. Chechik, G. (2003) Neural Comput. 15, 1481-1510.
22. Turrigiano, G. G. & Nelson, S. B. (2004) Nat. Rev. Neurosci. 5, 97-107.
23. Intrator, N. & Cooper, L. N. (1992) Neural Networks 5, 3-17.
24. Cover, T. M. & Thomas, J. A. (1991) Elements of Information Theory (Wiley, New York).
25. Jolivet, R., Lewis, T. J. & Gerstner, W. (2004) J. Neurophsyiol. 92, 959-976.
26. Beggs, J. M. (2000) Neural Comput. 13, 87-111.
27. Toyoizumi, T., Pfister, J.-P., Aihara, K. & Gerstner, W. (2005) in Advances in Neural Information Processing Systems, eds. Saul, L. K., Weiss, Y. & Bottou, L. (MIT Press, Cambridge, MA), Vol. 17, pp. 1409-1416.
28. Bell, A. J. & Parra, L. C. (2005) in Advances in Neural Information Processing Systems, eds. Saul, L. K., Weiss, Y. & Bottou, L. (MIT Press, Cambridge, MA), Vol. 17, pp. 121-128.
29. Bohte, S. M. & Mozer, M. C. (2005) in Advances in Neural Information Processing Systems, eds. Saul, L. K., Weiss, Y. & Bottou, L. (MIT Press, Cambridge, MA), Vol. 17, pp. 201-208.
30. Gerstner, W., Kempter, R., vanHemmen, J. L. & Wagner, H. (1996) Nature 383, 76-78.
31. Song, S., Miller, K. D. & Abbott, L. F. (2000) Nat. Neurosci. 3, 919-926.
32. Froemke, R. & Dan, Y. (2002) Nature 416, 433-438.
33. Bi, G.-Q. & Wang, H.-X. (2002) Physiol. Behav. 77, 551-555.
34. Senn, W. (2002) Biol. Cybern. 87, 344-355.
35. Sjöström, P. J., Turrigiano, G. G. & Nelson, S. B. (2001) Neuron 32, 1149-1164.