Spike-timing-dependent plasticity and relevant mutual information maximization

Neural Computation

Volumn 15, Issue 7, 2003, Pages 1481-1510

  Chechik, Gal ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ARTICLE; BIOLOGICAL MODEL; NERVE CELL PLASTICITY; PHYSIOLOGY;

ACTION POTENTIALS; MODELS, NEUROLOGICAL; NEURONAL PLASTICITY;

EID: 0037822252     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976603321891774     Document Type: Article

References (44)

1. Abbott, L., & Song, S. (1999). Temporally asymmetric Hebbian learning, spike timing and neural respons variability. In S. A. Solla & D. A. Cohen (Eds.), Advances in neural information processing systems, 11 (pp. 69-75). Cambridge, MA: MIT Press.
2. Abbott, L., Varela, J., Sen, K., & Nelson, S. (1997). Synaptic depression and cortical gain control. Science, 275, 220-223.
3. Aharonov, R., Guttig, R., & Sompolinsky, H. (2001). Generalized synaptic updating in temporally asymmetric Hebbian learning. In J. Bower (Ed.), Computational neuroscience: Trends in research 2001. Amsterdam: Elsevier.
4. Bell, C., Han, V., Sugawara, Y., & Grant, K. (1997). Synaptic plasticity in a cerebellum-like structure depends on temporal order. Nature, 387, 278-281.
5. Bi, Q., & Poo, M. (1999). Precise spike timing determines the direction and extent of synaptic modifications in cultured hippocampal neurons. J. Neurosci., 18, 10464-10472.
6. Chechik, G., Horn, D., & Ruppin, E. (2002). Hebbian learning and neuronal regulation. In M. A. Arbib (Ed.), Handbook of brain theory and neural networks (2nd ed.). Cambridge, MA: MIT Press.
7. Chechik, G., Meilijson, I., & Ruppin, E. (2001). Effective neuronal learning with ineffective Hebbian learning rules. Neural Computation, 23(4), 817-840.
8. Cover, T., & Thomas, J. (1991). Elements of information theory. New York: Wiley.
9. Dan, Y., & Poo, M. (1998). Hebbian depression of isolated neuromuscular synapses in vitro. Science, 256, 1570-1573.
10. Dayan, P., & Willshaw, D. (1991). Optimizing synaptic learning rules in linear associative memories. Biol. Cyber., 65, 253.
11. Debanne, D., Gahwiler, B., & Thompson, S. (1994). Asynchronous pre- and post-synaptic activity induces associative long-term depression in area CA1 of the rat hippocampus in vitro. Proc. Natl. Acad. Sci., 91, 1148-1152.
12. Debanne, D., Gahwiler, B., & Thompson, S. (1998). Long-term synaptic plasticity between pairs of individual CA3 pyramidal cells in rat hippocampal slice cultures. J. Physiol., 507(1), 237-247.
13. Egger, V., Feldmeyer, D., & Sakmann, B. (1999). Coincidence detection and changes of synaptic efficacy in spiny stellate neurons in rat barrel cortex. Nature Neuroscience, 2, 1098-1105.
14. Feldman, D. (2000). Timing based LTP and LTD at vertical inputs to layer II/III pyramidal cells in rat barrel cortex. Neuron, 27, 45-56.
15. Ferguson, T. (1996). A course in large sample theory. London: Chapman and Hall.
16. Froemke, R., & Dan, Y. (2002). Spike-timing dependent synaptic modification induced by natural spike trains. Nature, 416, 433-438.
17. Holmgren, C., & Zilberter, Y. (2001). Coincident spiking activity induces long term changes in inhibition of neocortical pyramidal cells. J. Neuroscience, 21, 8270-8277.
18. Horn, D., Levy, N., Meilijson, I., & Ruppin, E. (2000). Distributed synchrony of spiking neurons in a Hebbian cell assembly. In S. Solla, T. Leen, & K. Muller (Eds.), Advances in neural information processing systems, 12 (pp. 129-135). Cambridge, MA: MIT Press.
19. Kempter, R., Gerstner, W., & van Hemmen, J. (1999). Hebbian learning and spiking neurons. Phys. Rev. E, 59(4), 4498-4514.
20. Kempter, R., Gerstner, W., & van Hemmen, J. (2001). Intrinsic stabilization of output rates by spike-time dependent Hebbian learning. Neural Computation, 13, 2709-2742.
21. Kingman, J. (1993). Poisson processes. New York: Oxford University Press.
22. Lehman, E. (1998). Elements of large sample theory. New York: Springer-Verlag.
23. Levy, W., & Steward, D. (1983). Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neurosci., 8, 791-797.
24. Linsker, R. (1988). Self-organization in a perceptual network. Computer, 21(3), 105-117.
25. Linsker, R. (1992). Local synaptic learning rules suffice to maximize mutual information in a linear network. Neural Computation, 4(5), 691-702.
26. Magee, J., & Johnston, D. (1997). A synaptic controlled associative signal for Hebbian plasticity in hippocampal neurons. Science, 275(5297), 209-213.
27. Markram, H., Lubke, J., Frotscher, M., & Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275(5297), 213-215.
28. Mehta, M., Quirk, M., & Wilson, M. (1999). From hippocampus to VI: Effect of LTP on spatio-temporal dynamics of receptive fields. In J. Bower (Ed.), Computational neuroscience: Trends in research 1999. Amsterdam: Elsevier.
29. Monyer, H., Burnashev, N., Laurie, D., Sakmann, B., & Seeburg, P. (1994). Developmental and regional expression in the rat brain and functional properties of four NMDA receptors. Neuron, 12, 529-540.
30. Nishiyama, M., Hong, K., Katsuhiko, M., Poo, M., & Kato, K. (2000). Calcium stores regulate the polarity and input specificity of synaptic modification. Nature, 408, 584-588.
31. Paulsen, O., & Sejnowski, T. (2000). Natural patterns of activity and long term synaptic plasticity. Current Opinion in Neurobiology, 10, 172-179.
32. Rao, R., & Sejnowski, T. (2000). Predictive sequence learning in recurrent neocortical circuits. In S. Solla, T. Leen, & K. Muller (Eds.), Advances in neural information processing systems, 12 (pp. 164-170). Cambridge, MA: MIT Press.
33. Roberts, P. D., & Bell, C. C. (2002). Spike-timing dependent synaptic plasticity in biological systems. Biol Cybern., 87, 392-403.
34. Rubin, J., Lee, D., & Sompolinsky, H. (2001). Equilibrium properties of temporally asymmetric Hebbian plasticity. Phys. Rev., 86(2), 364-367.
35. Schultz, W., Dayan, P., & Montague, P. (1997), A neural subtrate of prediction and reward. Science, 275, 1593-1599.
36. Segal, M., & Andersen, P. (2000). Dendritic spines shaped by synaptic activity. Curr. Opin. Neurobiol., 10, 582-587.
37. Segev, L., & London, M. (2000). Untangling dendrites with quantitative models. Science, 290, 744-750.
38. Sejnowski, T. (1977). Statistical constraints on synaptic plasticity. J. Theo. Biol., 69, 385-389.
39. Shannon, C. (1948). A mathematical theory of communication. Bell Syst. Tech. J., 27, 379-423.
40. Singer, W., & Gray, C. (1995). Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci., 18, 555-586.
41. Song, S., Miller, K., & Abbott, L. (2000). Competitive Hebbian learning through spike-timing dependent synaptic plasticity. Nature Neuroscience, 3, 919-926.
42. Tang, Y. P., Shimizu, E., Dube, G. R., Rampon, C., Kerchner, G. A., Zhuo, M., Liu, G., & Tsien, J. Z. (1999). Genetic enhancement of learning and memory in mice. Nature, 401, 63-69.
43. Yao, H., & Dan, Y. (2001). Stimulus timing-dependent plasticity in cortical processing of orientation. Neuron, 32, 315-323.
44. Zhang, L., Tao, H. W., Holt, C., Harris, W., & Poo, M. (1998). A critical window for cooperation and competition among developing, retinotectal synapses. Nature, 395(3), 37-44.