Differences in Spiking Patterns among Cortical Neurons

Neural Computation

Volumn 15, Issue 12, 2003, Pages 2823-2842

  Shinomoto, Shigeru ^a   Shima, Keisetsu ^b   Tanji, Jim ^b  

^a KYOTO UNIVERSITY   (Japan)

^b TOHOKU UNIVERSITY SCHOOL OF MEDICINE   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ANIMAL; ARTICLE; BRAIN CORTEX; CLASSIFICATION; CYTOLOGY; EVOKED RESPONSE; GENETIC VARIABILITY; HAPLORHINI; NERVE CELL; PHYSIOLOGY; REACTION TIME; STATISTICAL DISTRIBUTION; SYNAPTIC TRANSMISSION; TIME; WAKEFULNESS;

ACTION POTENTIALS; ANIMALS; CEREBRAL CORTEX; EVOKED POTENTIALS; HAPLORHINI; NEURONS; REACTION TIME; STATISTICAL DISTRIBUTIONS; SYNAPTIC TRANSMISSION; TIME FACTORS; VARIATION (GENETICS); WAKEFULNESS;

EID: 0242550879     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976603322518759     Document Type: Article

References (23)

1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Trans. Autom, Contr. AC-19, 716-723.
2. Bak, M.J., & Schmidt, E. M. (1977). An improved time-amplitude window discriminator. IEEE Trans Biomed Eng., 24, 486-489.
3. Banfield, J. D., & Raftery, A. (1993). Model-based gaussian and non-gaussian clustering. Biometrics, 49, 803-821.
4. Bozdogan, H. (1992). Choosing the number of component clusters in the mixture-model using a new informational complexity criterion of the inverse-Fisher matrix. In O. Opitz, B. Lausen, & R. Kler (Eds.), Information and Classification, Proceedings of the 16th Annual Conference of the Gesellschaft Klassifikation e.V (pp. 40-54). New York: Springer-Verlag.
5. Bozdogan, H., & Sclove, S. L. (1984). Multi-sample cluster analysis using Akaike's information criterion. Annals of the Institute of the Statistical Mathematics, 36, 163-180.
6. Buzsaki, G., Leung, L. S., & Vanderwolf, C. H. (1983). Cellular bases of hip-pocampal EEC in the behaving rat. Brain Research Reviews, 6, 139-171.
7. Constantinidis, C. Williams, G. V., & Goldman-Rakic, P. S. (2002). A role for inhibition in shaping the temporal flow of information in prefrontal cortex. Nature Neuroscience, 5, 175-180.
8. Csicsvari, J., Hirase, H., Czurko, A., & Buzsaki, G. (1998). Reliability and state dependence of pyramidal cell-interneuron synapses in the hippocampus: An ensemble approach in the behaving rat. Neuron, 21, 179-189.
9. Evarts, E. V. (1968). A technique for recording activity of subcortical neurons in moving animals. Electroencephalogr, Clin. Neurophysiol., 24, 83-86.
10. Fuster, J. M. (1997). The prefrontal cortex: Anatomy, physiology, and neuropsychology of the frontal lobe. New York: Lippincott-Raven.
11. Garey, L. (1999). Broadmann's localisation in the cerebral cortex. London: Imperial College Press.
12. Holt, G. R., Softky, W. R., Koch, C., & Douglas, R. J. (1996). Comparison of discharge variability in vitro and in vivo in cat visual cortex neurons. J. Neurophysiology, 75, 1806-1814.
13. Lindsay, B. G. (1995). Mixture models: Theory, geometry and applications. Hayward, CA: Institute of Mathematical Statistics.
14. Parent, A. (1996). Carpenter's human neuroanatomy (9th ed.). Baltimore, MD: Williams & Wilkins.
15. Ranck, J. B., Jr. (1973). Studies on single neurons in dorsal hippocampal formation and septum in unrestrained rats. Part I. Behavioral correlates and firing repertoires. Exp. Neurol., 41, 461-531.
16. Sakai, Y., Funahashi, S., & Shinomoto, S. (1999). Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons. Neural Networks, 12, 1181-1190.
17. Shima, K., Sawamura, H., & Tanji, J. (2001). Neuronal activity in the supplementary and pre-supplementary motor areas in a multiple waiting-period task. In Proc. 31st Annu. Soc. Neurosci (p. 1932). San Deigo, CA: Society for Neuroscience.
18. Shima, K., & Tanji, J. (2000). Neuronal activity in the supplementary and presupplementary motor areas for temporal organization of multiple movements. J. Neurophysiol., 84, 2148-2160.
19. Shinomoto, S., Sakai, Y., & Funahashi, S. (1999). The Ornstein-Uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex. Neural Computation, 11, 935-951.
20. Shinomoto, S., Shima, K., & Tanji, J. (2002). New classification scheme of cortical sites with the neuronal spiking characteristics. Neural Networks, 15, 1165-1169.
21. Shinomoto, S., & Tsubo, Y. (2001). Modeling spiking behavior of neurons with time-dependent Poisson processes. Phys. Rev. E, 041910 (1-13).
22. Tanji, J. (1996). New concepts of the supplementary motor area. Curr. Opin. Neurobiol., 6, 782-787.
23. Tiesinga, P. H. E., Fellous, J. M., & Sejnowski, T. J. (2002). Attractor reliability reveals deterministic structure in neuronal spike trains. Neural Computation, 14, 1629-1650.