Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold

Frontiers in Computational Neuroscience

Volumn 3, Issue JUL, 2009, Pages

  Kobayashi, Ryota ^a   Tsubo, Yasuhiro ^b   Shinomoto, Shigeru ^c  

^a RITSUMEIKAN UNIVERSITY   (Japan)

^b RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

^c KYOTO UNIVERSITY   (Japan)

Author keywords

Adaptive threshold; Brain simulato; Cortical neuron; Fluctuating input; Leaky integrate and fire model; Parameter optimization; Predicting spike times

Indexed keywords

ADAPTIVE THRESHOLDS; CORTICAL NEURONS; FLUCTUATING INPUT; LEAKY INTEGRATE-AND-FIRE MODEL; PARAMETER OPTIMIZATION; PREDICTING SPIKE TIMES;

BRAIN; DISTRIBUTED COMPUTER SYSTEMS; FORECASTING; NEURAL NETWORKS;

NEURONS;

EID: 77953482919     PISSN: 16625188     EISSN: None     Source Type: Journal    
DOI: 10.3389/neuro.10.009.2009     Document Type: Article

References (62)

1. Abeles, M. (1991). Corticonics: Neural Circuits of the Cerebral Cortex. Cambridge, Cambridge University Press.
2. Achard, P., and De Schutter, E. (2006). Complex parameter landscape for a complex neuron model. PLoS Comput. Biol. 2, e94.
3. Azouz, R., and Gray C. M. (2000). Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neurons in vivo. Proc. Natl. Acad. Sci. USA 97, 8110-8115.
4. Badel, L., Lefort, S., Brette, R., Petersen, C. C. H., Gerstner, W., and Richardson, M. J. E. (2008). Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. J. Neurophysiol. 99, 656-666.
5. Beggs, J. M., and Plenz, D. (2003). Neuronal avalanches in neocortical circuits. J. Neurosci. 23, 11167-11177.
6. Bower, J. M., and Beeman, D. (1995). The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System. New York, Springer-Verlag.
7. Brandman, R., and Nelson, M. E. (2002). A simple model of long-term spike train regularization. Neural Comput. 14, 1575-1597.
8. Brette, R., and Gerstner, W. (2005). Adaptive exponential integrate-andfire model as an effective description of neuronal activity. J. Neurophysiol. 94, 3637-3642.
9. Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., Diesmann, M., Morrison, A., Goodman, P. H., Harris, F. C., Zirpe, M., Natschläger, T., Pecevski, D., Ermentrout, B., Djurfeldt, M., Lansner, A., Rochel, O., Vieville, T., Muller, E., Davison, A. P., El Boustani, S., and Destexhe, A. (2007). Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23, 349-398.
10. Bryant, H. L., and Segundo, J. P. (1976). Spike initiation by transmembrane current: a white-noise analysis. J. Physiol. 260, 279-314.
11. Bugmann, G., Christodoulou, C., and Taylor, J. G. (1997). Role of temporal integration and fluctuation detection in the highly irregular firing of a leaky integrator neuron model with partial reset. Neural Comput. 9, 985-1000.
12. Chacron, M. J., Pakdaman, K., and Longtin, A. (2003). Interspike interval correlations, memory, adaptation, and refractoriness in a leaky integrate-andfire model with threshold fatigue. Neural Comput. 15, 253-278.
13. Destexhe, A., and Paré, D. (1999). Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. J. Neurophysiol. 81, 1531-1547.
14. Diesmann, M., Gewaltig, M. O., and Aertsen, A. (1999). Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529-533.
15. Fourcaud-Trocmé, N., Hansel, D., van Vreeswijk, C., and Brunel, N. (2003). How spike generation mechanisms determine the neuronal response to fluctuating inputs. J. Neurosci. 23, 11628-11640.
16. Geisler, C. D., and Goldberg, J. M. (1966). A stochastic model of the repetitive activity of neurons. Biophys. J. 6, 53-69.
17. Gerstner, W., and Kistler, W. M. (2002). Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge, Cambridge University Press.
18. Gewaltig, M-O., and Diesmann, M. (2007). NEST. Scholarpedia 2, 1430.
19. Goldman, M. S., Golowasch, J., Marder, E., and Abbott, L. F. (2001). Global structure, robustness, and modulation of neuronal models. J. Neurosci. 21, 5229-5238.
20. Gray, C. M., and McCormick, D. A. (1996) Chattering cells: superficial pyramidal neurons contributing to the generation of synchronous oscillations in the visual cortex. Science 274, 109-113.
21. Hansel, D., and Sompolinsky, H. (1998). Modeling feature selectivity in local cortical circuits. In Methods in Neuronal Modeling, 2nd Edn., C. Koch and I. Segev, eds (Cambridge, MA, MIT Press).
22. Hille, B. (2001). Ion Channels of Excitable Membranes. Sunderland, MA, Sinauer Associates.
23. Hines, M. L., and Carnevale, N. T. (1997). The NEURON simulation environment. Neural Comput. 9, 1179-1209.
24. Hodgkin, A. L., and Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500-544.
25. Huys, Q. J. M., Ahrens, M. B., and Paninski, L. (2006). Efficient estimation of detailed single-neuron models. J. Neurophysiol. 96, 872-890.
26. Ikegaya, Y., Aaron, G., Cossart, R., Aronov, D., Lampl, I., Ferster, D., and Yuste, R. (2004). Synfire chains and cortical songs: temporal modules of cortical activity. Science 304, 559-564.
27. Inoue, J., Sato, S., and Ricciardi, L. M. (1995). On the parameter estimation for diffusion models of single neuron's activities. Biol. Cybern. 73, 209-221.
28. Izhikevich, E. M. (2004). Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15, 1063-1070.
29. Izhikevich, E. M., and Edelman, G. M. (2008). Large-scale model of mammalian thalamocortical systems. Proc. Natl. Acad. Sci. USA 105, 3593-3598.
30. Jolivet, R., Kobayashi, R., Rauch, A., Naud, R., Shinomoto, S., and Gerstner, W. (2008a). A benchmark test for a quantitative assessment of simple neuron models. J. Neurosci. Methods 169, 417-424.
31. Jolivet, R., Lewis, T. J., and Gerstner, W. (2004). Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy. J. Neurophysiol. 92, 959-976.
32. Jolivet, R., Rauch, A., Lüscher, H-R., and Gerstner, W. (2006). Predicting spike timing of neocortical pyramidal neurons by simple threshold models. J. Comput. Neurosci. 21, 35-49.
33. Jolivet, R., Schürmann, F., Berger, T. K., Naud, R., Gerstner, W., and Roth, A. (2008b). The quantitative singleneuron modeling competition. Biol. Cybern. 99, 417-426.
34. Kobayashi, R., and Shinomoto, S. (2007). State space method for predicting the spike times of a neuron. Phys. Rev. E 75, 011925.
35. Koch, C. (1999). Biophysics of Computation: Information Processing in Single Neurons. Oxford, Oxford University Press.
36. Lansky, P., and Ditlevsen, S. (2008). A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models. Biol. Cybern. 99, 253-262.
37. Lansky, P., Sanda, P., and He, J. (2006). The parameters of the stochastic leaky integrate-and-fire neuronal model. J. Comput. Neurosci. 21, 211-223.
38. Larkum, M. E., Senn, W., and Lüscher, H. R. (2004). Top-down dendritic input increases the gain of layer 5 pyramidal neurons. Cereb. Cortex 14, 1059-1070.
39. Mainen, Z. F., and Sejnowski, T. J. (1995). Reliability of spike timing in neocortical neurons. Science 268, 1503-1506.
40. Markram H. (2006). The blue brain project. Nat. Rev. Neurosci. 7, 153-160.
41. McCormick, D. A., Connors, B. W., Lighthall, J. W., and Prince, D. A. (1985). Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. J. Neurophysiol. 54, 782-806.
42. McIntyre, C. C., Grill, W. M., Sherman, D. L., and Thakor, N. V. (2004). Cellular effects of deep brain stimulation: model-based analysis of activation and inhibition. J. Neurophysiol. 91, 1457-1469.
43. Mihalas, S., and Niebur, E. (2009). A generalized linear integrate-and-fire neural model produces diverse spiking behaviors. Neural Comput. 21, 704-718.
44. Miura, K., Tsubo, Y., Okada, M., and Fukai, T. (2007). Balanced excitatory and inhibitory inputs to cortical neurons decouple firing irregularity from rate modulations. J. Neurosci. 27, 13802-13812.
45. Nelder, J. A., and Mead, R. (1965). A simplex method for function minimization. Comput. J. 7, 308-313.
46. Nowak, L. G., Azouz, R., Sanchez-Vives, M. V., Gray, C. M., and McCormick, D. A. (2003). Electrophysiological classes of cat primary visual cortical neurons in vivo as revealed by quantitative analyses. J. Neurophysiol. 89, 1541-1566.
47. Pinsky, P. F., and Rinzel, J. (1994). Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons. J. Comput. Neurosci. 1, 39-60.
48. Plesser, H. E., and Diesmann, M. (2009). Simplicity and efficiency of integrate-and-fire neuron models. Neural Comput. 21, 353-359.
49. Robinson, H. P. C., and Kawai, N. (1993). Injection of digitally synthesized synaptic conductance transients to measure the integrative properties of neurons. J. Neurosci. Methods 49, 157-165.
50. Sharp, A. A., O'Neil, M. B., Abbott, L. F., and Marder, E. (1993). The dynamic clamp: artificial conductances in biological neurons. Trends Neurosci. 16, 389-394.
51. Shinomoto, S., Kim, H., Shimokawa, T., Matsuno, N., Funahashi, S., Shima, K., Fujita, I., Tamura, H., Doi, T., Kawano, K., Inaba, N., Fukushima, K., Kurkin, S., Kurata, K., Taira, M., Tsutsui, K., Komatsu, H., Ogawa, T., Koida, K., Tanji, J., and Toyama, K. (2009). Relating neuronal firing patterns to functional differentiation of cerebral cortex. PLoS Comput. Biol. 5, e1000433.
52. Shinomoto, S., Sakai, Y., and Funahashi, S. (1999). The Ornstein-Uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex. Neural Comput. 11, 935-951.
53. Stein, R. B. (1965). A theoretical analysis of neuronal variability. Biophys. J. 5, 173-194.
54. Stevens, C. F., and Zador, A. M. (1998). Input synchrony and the irregular firing of cortical neurons. Nat. Neurosci. 1, 210-217.
55. Traub, R. D., Contreras, D., Cunningham, M. O., Murray, H., LeBeau, F. E., Roopun, A., Bibbig, A., Wilent, W. B., Higley, M. J., and Whittington, M. A. (2005). Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. J. Neurophysiol. 93, 2194-2232.
56. Troyer, T. W., and Miller, K. D. (1997). Physiological gain leads to high ISI variability in a simple model of a cortical regular spiking cell. Neural Comput. 9, 971-983.
57. Tsubo, Y., Kaneko, T., and Shinomoto, S. (2004). Predicting spike timings of current-injected neurons. Neural Netw. 17, 165-173.
58. Tsubo, Y., Takada, M., Reyes, A. D., and Fukai, T. (2007). Layer and frequency dependencies of phase response properties of pyramidal neurons in rat motor cortex. Eur. J. Neurosci. 25, 3429-3441.
59. Tuckwell, H. C., and Richter, W. (1978). Neuronal interspike time distribution and the estimation of neurophysiological and neuroanatomical parameters. J. Theor. Biol. 71, 167-183.
60. Uhlenbeck, G. E., and Ornstein, L. S. (1930). On the theory of the Brownian motion. Phys. Rev. 36, 823-841.
61. Victor, J. D., and Purpura, K. P. (1996). Nature and precision of temporal coding in visual cortex: a metricspace analysis. J. Neurophysiol. 76, 1310-1326.
62. Yang, C. R., Seamans, J. K., and Gorelova, N. (1996). Electrophysiological and morphological properties of layers V-VI principal pyramidal cells in rat prefrontal cortex in vitro. J. Neurosci. 16, 1904-1921.