Spike-timing-dependent plasticity and reliability optimization: The role of neuron dynamics

Neural Computation

Volumn 23, Issue 7, 2011, Pages 1768-1789

  Pool, R Rossi ^a   Mato, G ^a  

^a Facultad de Ciencias Naturales e Instituto M Lillo   (Argentina)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; BIOLOGICAL MODEL; COMPARATIVE STUDY; ENTROPY; LETTER; NERVE CELL; NERVE CELL PLASTICITY; PHYSIOLOGY; RANDOMIZATION; REPRODUCIBILITY; TIME;

ACTION POTENTIALS; ENTROPY; MODELS, NEUROLOGICAL; NEURONAL PLASTICITY; NEURONS; RANDOM ALLOCATION; REPRODUCIBILITY OF RESULTS; TIME FACTORS;

EID: 79959597731     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00140     Document Type: Letter

References (47)

1. Abbott, L. F., & Nelson, S. B. (2000). Synaptic plasticity: Taming the beast. Nature Neuroscience, 3, 1178-1183.
2. Abramowitz, M., & Stegun, I. A. (Eds.) (1965). Handbook of mathematical functions with formulas, graphs, and mathematical tables. New York: Dover.
3. Bazhenov, M., Rulkov, N. F., Fellous, J. M., & Timofeev, I. (2005) Role of network dynamics in shaping spike timing reliability. Physical Review E, 72, 041903, 1-5.
4. Bell, A., & Parra, L. (2005). Maximizing sensitivity in a spiking network. In L. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in neural information processing systems, 17 (pp. 121-128). Cambridge, MA: MIT Press.
5. Bell, C. C., Han, V. Z., Sugawara, Y., & Grant, K. (1997). Synaptic plasticity in a cerebellum-like structure depends on temporal order. Nature, 387, 278-281.
6. Bi, G. Q., & Poo, M. M. (1998). Synaptic modications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience, 18, 10464-10472.
7. Bohte, S. M., & Mozer, M. M. (2007). Reducing spike train variability: A computational theory of spike-timing dependent plasticity. Neural Computation, 19, 371-403.
8. Brown, E., Moehlis, J., & Holmes, P. (2004). On the phase reduction and response dynamics of neural oscillator populations. Neural Computation, 16, 673-715.
9. Caporale, N., & Dan, Y. (2008). Spike-timing dependent plasticity: A Hebbian learning rule. Annu. Rev. Neurosci., 31, 25-46.
10. Chechik, G. (2003). Spike time dependent plasticity and information maximization. Neural Computation, 15, 1481-1510.
11. Dan, Y.,&Poo, M.-M. (1998).Hebbian depression of isolated neuromuscular synapse in vitro. Science, 256, 1570-1573.
12. Dan, Y., & Poo, M.-M. (2004). Spike timing-dependent plasticity of neural circuits. Neuron, 44, 23-30.
13. Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling of neural systems. Cambridge, MA: MIT Press.
14. Debanne, D., Gahwiler, B. H., & Thompson, S. M. (1998). Long-term synaptic plasticity between pairs of individual CA3 pyramidal cells in rat hippocampal slice cultures. Journal of Physiology, 507(Pt. 1), 237-247.
15. Egger, V., Feldmeyer, D., & Sakmann, B. (1999). Coincidence detection and efficacy changes in synaptic connections between spiny stellate neurons of the rat barrel cortex. Nature Neuroscience, 2, 1098-1105.
16. Erisir, A., Lau, D., Rudy, B., & Leonard, C. S. (1999). Function of specific K+ channels in sustained high-frequency firing of fast-spiking neocortical interneurons. Journal of Neurophysiology, 8, 2476-2489.
17. Feldman, D. E. (2000). Timing-based LTP and LTD at vertical inputs to layer II/III pyramidal cells in rat barrel cortex. Neuron, 27, 45-56.
18. Froemke, R. C., & Dan, Y. (2002). Spike-timing-dependent synaptic modication induced by natural spike trains. Nature, 416, 433-438.
19. Galán, R. F., Ermentrout, G. B., & Urban, N. N. (2007). Reliability and stochastic synchronization in type I vs. type II neural oscillators. Neurocomputing, 70, 2102-2106.
20. Gerstner, W. (2001). A framework for spiking neuron models: The spike response model. In F. Moss & S. Gielen (Eds.), The handbook of biological physics (Vol. 4, pp. 469-516). Amsterdam: North-Holland.
21. Hansel, D., &Mato, G. (2003). Asynchronous states and the emergence of synchrony in large networks of interacting excitatory and inhibitory neurons. Neural Computation, 15, 1-56.
22. Hennequin, G., Gerstner, W., & Pfister, J. (2010). STDP in adaptive neurons gives close-to-optimal information transmission. Front. Comput. Neurosci., 4, 143.
23. Izhikevich, E. (2001). Resonate and fire neurons. Neural Networks, 14, 833-894.
24. Izhikevich, E. (2007). Dynamical systems in neuroscience: The geometry of excitability and bursting. Cambridge, MA: MIT Press.
25. Karmarkar, U. R., & Buonomano, D. V. (2002). A model of spike-timing dependent plasticity: One or two coincidence detectors? Journal of Neurophysiology, 88, 507- 513.
26. Kepecs, A., van Rossum, M. C. W., Song, S., & Tegner, J. (2002). Spike-timingdependent plasticity:Commonthemes and divergent vistas. Biological Cybernetics, 87, 446-458.
27. Latham, P. E., Richmond, B. J., Nelson, P. G., & Nirenberg, S. (2000). Intrinsic dynamics in neuronal networks. I. Theory. Journal of Neurophysiology, 83, 808-827.
28. Lengyel, M., Kwag, J., Paulsen, O., & Dayan, P. (2005). Matching storage and recall: Hippocampal spike timing-dependent plasticity and phase response curves. Nature Neuroscience, 8, 1677-1683.
29. Levy, W. B., & Steward, O. (1983). Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neuroscience, 8, 791- 797.
30. Magee, J. C., & Johnston, D. (1997). A synaptically controlled, associative signal for Hebbian plasticity in hippocampal neurons. Science, 275, 209-213.
31. Malenka, R. C., & Bear, M. F. (2004). LTP and LTD: An embarrassment of riches. Neuron, 44, 5-21.
32. Markram, H., Lubke, J., Frotscher, M., & Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275, 213-215.
33. Pfeuty, B., Mato, G., Golomb, D., & Hansel, D. (2003). Electrical synapses and synchrony: The role of intrinsic currents. Journal of Neuroscience, 23, 6280-6294.
34. Pfister, J.-P., Toyoizumi, T., Barber, D., & Gerstner, W. (2006). Optimal spike-timing dependent plasticity for precise action potential ring in supervised learning. Neural Computation, 18, 1318-1348.
35. Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1992). Numerical recipes in FORTRAN 77: The art of scientific computing. Cambridge: Cambridge University Press.
36. Roberts, P. D., & Bell, C. C. (2002). Spike timing dependent synaptic plasticity in biological systems. Biological Cybernetics, 87, 392-403.
37. Rubin, J., Lee, D.D., & Sompolinsky, H. (2001). Equilibrium properties of temporally asymmetric hebbian plasticity. Physical Review Letters, 86, 364-367.
38. Sharp, A. A., O'Neil, M. B., Abbott, L. F., & Marder, E. (1993). The dynamic clamp: Artificial conductances in biological neurons. Trends Neurosci., 16, 389-394.
39. Sjöström, P. J., Turrigiano, G. G.,&Nelson, S. B. (2001) Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron, 32, 1149-1164.
40. Song, S., & Abbott, L. F. (2001). Column and map development and cortical remapping through spike-timing dependent plasticity. Neuron, 32, 339-350
41. Tateno, T.,&Robinson,H. P. (2006). Rate coding and spike-time variability in cortical neurons with two types of threshold dynamics. Journal of Neurophysiology, 95, 2650-2663.
42. Toyoizumi, T., Pfister, J.-P., Aihara, K.,&Gerstner,W. (2005). Spike-timing dependent plasticity and mutual information maximization for a spiking neuron model. In L. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in neural information processing systems, 17 (pp. 1409-1416). Cambridge, MA: MIT Press.
43. Toyoizumi, T., Pfister, J.-P., Aihara, K., & Gerstner, W. (2007). Optimality model of unsupervised spike-timing-dependent plasticity: Synaptic memory and weight distribution. Neural Computation, 19, 639-671.
44. Wang, X.-J. (2010). Neurophysiological and computational principles of cortical rhythms in cognition. Physiol. Rev., 90, 1195-1268.
45. Xie, X., & Seung, S. (2000). Spike-based learning rules and stabilization of persistent neural activity in recurrent neocortical circuits. In S. A. Solla, T. K. Leen, & K.-R. Muller (Eds.), Advances in neural information processing systems, 12 (pp. 199-205). Cambridge, MA: MIT Press.
46. Yang, S. N., Tang, Y. G., & Zucker, R. S. (1999). Selective induction of LTP and LTD by postsynaptic [Ca2+]i elevation. Journal of Neurophysiology, 81, 781-787.
47. Zhang, L. I., Tao,H.W., Holt, C. E., Harris,W. A.,&Poo, M. (1998).Acritical window for cooperation and competition among developing retinotectal synapses. Nature, 395, 37-44.