Finding the event structure of neuronal spike trains

Neural Computation

Volumn 23, Issue 9, 2011, Pages 2169-2208

  Vincent Toups, J ^a   Fellous, Jean Marc ^b   Thomas, Peter J ^c,d   Sejnowski, Terrence J ^e,f   Tiesinga, Paul H ^a,g  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

^b The University of Arizona   (United States)

^c CASE WESTERN RESERVE UNIVERSITY   (United States)

^d OBERLIN COLLEGE   (United States)

^e HOWARD HUGHES MEDICAL INSTITUTE   (United States)

^f UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^g RADBOUD UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ALGORITHM; ANIMAL; ARTICLE; BIOLOGICAL MODEL; BRAIN; FUZZY LOGIC; NERVE CELL; ORGAN CULTURE TECHNIQUE; PATCH CLAMP; PHYSIOLOGY; RAT; SPRAGUE DAWLEY RAT;

ACTION POTENTIALS; ALGORITHMS; ANIMALS; BRAIN; FUZZY LOGIC; MODELS, NEUROLOGICAL; NEURONS; ORGAN CULTURE TECHNIQUES; PATCH-CLAMP TECHNIQUES; RATS; RATS, SPRAGUE-DAWLEY;

EID: 80051601956     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00173     Document Type: Article

References (47)

1. Bair, W., & Koch, C. (1996). Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey. Neural Comput., 8, 1185-1202.
2. Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum.
3. Bouguessa, M., Wang, S. R., & Sun, H. J. (2006). An objective approach to cluster validation. Pattern Recognition Letters, 27, 1419-1430.
4. Buia, C., & Tiesinga, P. (2006). Attentional modulation of firing rate and synchrony in a model cortical network. Journal of Computational Neuroscience, 20, 247-264.
5. Buracas, G. T., Zador, A. M., DeWeese, M. R., & Albright, T. D. (1998). Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex. Neuron, 20, 959-969.
6. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. Hoboken, NJ: Wiley.
7. de la Rocha, J., Doiron, B., Shea-Brown, E., Josic, K., & Reyes, A. (2007). Correlation between neural spike trains increases with firing rate. Nature, 448, 802-806.
8. Duda, R.O., Hart, P. E.,&Stork, D. G. (2001). Pattern classification (2nd ed.).Hoboken, NJ:Wiley.
9. Ermentrout, G. B., Galan, R. F., & Urban, N. N. (2008). Reliability, synchrony and noise. Trends Neurosci., 31, 428-434.
10. Fellous, J.M., Houweling, A. R.,Modi, R. H., Rao, R. P., Tiesinga, P. H., & Sejnowski, T. J. (2001). Frequency dependence of spike timing reliability in cortical pyramidal cells and interneurons. J. Neurophysiol., 85, 1782-1787.
11. Fellous, J. M., Tiesinga, P. H., Thomas, P. J., & Sejnowski, T. J. (2004). Discovering spike patterns in neuronal responses. J. Neurosci., 24, 2989-3001.
12. Green, D.M., & Swets, J. A. (1966). Signal detection theory and psychophysics. Hoboken, NJ:Wiley.
13. Haider, B., & McCormick, D. A. (2009). Rapid neocortical dynamics: Cellular and network mechanisms. Neuron, 62, 171-189.
14. Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol., 160, 106-154.
15. Jolivet, R., Lewis, T. J., & Gerstner,W. (2004). Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailedmodel to a high degree of accuracy. J. Neurophysiol., 92, 959-976.
16. Jolivet, R., Rauch,A., Luscher, H. R.,&Gerstner,W. (2006). Predicting spike timing of neocortical pyramidal neurons by simple threshold models. J. Comput. Neurosci., 21, 35-49.
17. Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). New York: Springer.
18. Keat, J., Reinagel, P., Reid, R. C., & Meister, M. (2001). Predicting every spike: A model for the responses of visual neurons. Neuron, 30, 803-817.
19. Larsen, R. J., & Marx, M. L. (1986). An introduction to mathematical statistics and its applications (2nd ed.). Upper Saddle River, NJ: Prentice Hall.
20. Mainen, Z. F., & Sejnowski, T. J. (1995). Reliability of spike timing in neocortical neurons. Science, 268, 1503-1506.
21. Markowitz, D. A., Collman, F., Brody, C. D., Hopfield, J. J., & Tank, D. W. (2008). Rate-specific synchrony: Using noisy oscillations to detect equally active neurons. Proc. Natl. Acad. Sci. USA, 105, 8422-8427.
22. McCormick, D. A., Connors, B. W., Lighthall, J. W., & Prince, D. A. (1985). Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. J. Neurophysiol., 54, 782-806.
23. Mishra, J., Fellous, J. M., & Sejnowski, T. J. (2006). Selective attention through phase relationship of excitatory and inhibitory input synchrony in a model cortical neuron. Neural Netw., 19, 1329-1346.
24. Pal, N. R., & Bezdek, J. C. (1995). On cluster validity for the fuzzy C-means model. IEEE Transactions on Fuzzy Systems, 3, 370-379.
25. Panzeri, S., & Treves, A. (1995). Analytical estimates of limited sampling biases in different information measures. Network: Comput. Neural Syst., 7, 87-107.
26. Pillow, J. W., Paninski, L., Uzzell, V. J., Simoncelli, E. P., & Chichilnisky, E. J. (2005). Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J. Neurosci., 25, 11003-11013.
27. Powers, R. K.,Dai, Y., Bell, B. M., Percival, D. B.,&Binder,M.D. (2005).Contributions of the input signal and prior activation history to the discharge behaviour of rat motoneurones. J. Physiol., 562, 707-724.
28. Reinagel, P., Godwin, D., Sherman, S. M., & Koch, C. (1999). Encoding of visual information by LGN bursts. J. Neurophysiol., 81, 2558-2569.
29. Reinagel, P., & Reid, R. C. (2000). Temporal coding of visual information in the thalamus. J. Neurosci., 20, 5392-5400.
30. Reinagel, P., & Reid, R. C. (2002). Precise firing events are conserved across neurons. J. Neurosci., 22, 6837-6841.
31. Rezaee, M. R., Lelieveldt, B. P. F., & Reiber, J. H. C. (1998). A new cluster validity index for the fuzzy C-mean. Pattern Recognition Letters, 19, 237-246.
32. Rieke, F., Warland, D., de Ruyter van Steveninck, R. R., & Bialek, W. (1997). Spikes: Exploring the neural code. Cambridge, MA: MIT Press.
33. Rohatgi, V. K. (2003). Statistical inference. Mineola, NY: Dover.
34. Salinas, E., & Sejnowski, T. (2000). Impact of correlated synaptic input on output variability in simple neuronal models. J. Neurosci., 20, 6193-6209.
35. Salinas, E., & Sejnowski, T. J. (2001). Correlated neuronal activity and the flow of neural information. Nature Reviews Neuroscience, 2, 539-550.
36. Steriade,M. (2004). Neocortical cell classes are flexible entities. Nat. Rev. Neurosci., 5, 121-134.
37. Strong, S., Koberle, R., de Ruyter van Steveninck, R., & Bialek, W. (1998). Entropy and information in neural spike trains. Phys. Rev. Letts., 80, 197-200.
38. Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society Series B, Statistical Methodology, 63, 411-423.
39. Tiesinga, P. H., Fellous, J. M., & Sejnowski, T. J. (2002). Attractor reliability reveals deterministic structure in neuronal spike trains. Neural Comput., 14, 1629-1650.
40. Tiesinga, P., Fellous, J. M., & Sejnowski, T. J. (2008). Regulation of spike timing in visual cortical circuits. Nat. Rev. Neurosci., 9, 97-107.
41. Tiesinga, P. H. E., & Toups, J. V. (2005). The possible role of spike patterns in cortical information processing. Journal of Computational Neuroscience, 18, 275-286.
42. Toups, J. V., Fellous, J.-M., Thomas, P. J., Sejnowski, T. J., & Tiesinga, P. H. (2011). Multiple spike train patterns occur at bifurcation points of membrane potential dynamics. Unpublished manuscript.
43. Victor, J. D., & Purpura, K. P. (1996). Nature and precision of temporal coding in visual cortex: A metric-space analysis. J. Neurophysiol., 76, 1310-1326.
44. Wang, H. P., Spencer, D., Fellous, J. M., & Sejnowski, T. J. (2010). Synchrony of thalamocortical inputs maximizes cortical reliability. Science, 328, 106-109.
45. Womelsdorf, T., Schoffelen, J.M., Oostenveld, R., Singer,W., Desimone, R., Engel, A. K., et al. (2007). Modulation of neuronal interactions through neuronal synchronization. Science, 316, 1609-1612.
46. Yan, M., & Ye, K. (2007). Determining the number of clusters using the weighted gap statistic. Biometrics, 63, 1031-1037.
47. Zahid, N., Limouri, N., & Essaid, A. (1999). A new cluster-validity for fuzzy clustering. Pattern Recognition, 32, 1089-1097.