Improved similarity measures for small sets of spike trains

Neural Computation

Volumn 23, Issue 12, 2011, Pages 3016-3069

  Naud, Richard ^a   Gerhard, Felipe ^a   Mensi, Skander ^a   Gerstner, Wulfram ^a  

^a EPFL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ALGORITHM; ANIMAL; ARTICLE; BIOLOGICAL MODEL; BRAIN CORTEX; CYTOLOGY; HUMAN; NERVE CELL; ORGAN CULTURE TECHNIQUE; PHYSIOLOGY; PYRAMIDAL NERVE CELL; RAT; SIGNAL PROCESSING; STATISTICS; TIME;

ACTION POTENTIALS; ALGORITHMS; ANIMALS; CEREBRAL CORTEX; HUMANS; MODELS, NEUROLOGICAL; NEURONS; ORGAN CULTURE TECHNIQUES; PYRAMIDAL CELLS; RATS; SIGNAL PROCESSING, COMPUTER-ASSISTED; STOCHASTIC PROCESSES; TIME FACTORS;

EID: 84856394711     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00208     Document Type: Article

References (88)

1. Abeles, M. (1991). Corticonics. Cambridge: Cambridge University Press.
2. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In F. Cśaki&B. N. Petrov (Eds.), Proc. of the 2nd Int. Symp. on Information Theory (pp. 267-281). Budapest: Akademiai Kiado.
3. Arieli, A., Sterkin, A., Grinvald, A., & Aertsen, A. (1996). Dynamics of ongoing activity: Explanation of the large variability in evoked cortical responses. Science, 273, 1868-1871.
4. Badel, L., Lefort, S., Brette, R., Petersen, C., Gerstner,W., & Richardson, M.J.E. (2008). Dynamic IV curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. Journal of Neurophysiology, 99(2), 656-666.
5. Brown, E., Barbieri, R., Ventura, V., Kass, R., & Frank, L. (2002). The time-rescaling theorem and its application to neural spike train data analysis. Neural Computation, 14, 325-346.
6. Calvin, W., & Stevens, C. (1968). Synaptic noise and other sources of randomness in motoneuron interspike intervals. Journal of Neurophysiology, 31, 574-587.
7. Carandini, M. (2004). Amplification of trial-to-trial response variability by neurons in visual cortex. PLoS Biology, 2(9), 1483-1493.
8. Carnell, A., & Richardson, D. (2005). Linear algebra for time series of spikes. In M. Verleysen (Ed.), Proceedings of the 13th European Symposium on Artificial Neural Networks (pp. 363-368). Evere, Belgium: d-side.
9. Chi, Z., & Margoliash, D. (2001). Temporal precision and temporal drift in brain and behavior of zebra finch song. Neuron, 32(5), 899-910.
10. Chi, Z., Wu, W., Haga, Z., Hatsopoulos, N. G., & Margoliash, D. (2007). Templatebased spike pattern identification with linear convolution and dynamic time warping. Journal of Neurophysiology, 97(2), 1221-1235.
11. Christen,M., Kohn, A.,Ott, T., & Stoop, R. (2006).Measuring spike pattern reliability with the Lempel-Ziv-distance. J. Neurosci. Methods, 156(1-2), 342-350.
12. Croner, L. J., Purpura, K.,&Kaplan, E. (1993).Response variability in retinal ganglion cells of primates. Proc. Natl. Acad. Sci. USA, 90(17), 8128-8130.
13. Daley, D., & Vere-Jones, D. (1988). An introduction to the theory of point processes. New York: Springer.
14. David, S. V., & Gallant, J. L. (2005). Predicting neuronal responses during natural vision. Network, 16(2-3), 239-260.
15. de Ruyter van Steveninck, R. R., & Bialek, W. (1988). Real-time performance of a movement-sensitive neuron in the blowfly visual system: Coding and information transfer in short spike sequences. Proc. R. Soc. B, 234, 379-414.
16. DiLorenzo, P. M., & Victor, J. D. (2003). Taste response variability and temporal coding in the nucleus of the solitary tract of the rat. Journal of Neurophysiology, 90(3), 1418-1431.
17. Druckmann, S., Banitt, Y., Gidon, A., & Scḧurmann, F. (2007). A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Frontiers in Neuroscience, 1(1), 7-18.
18. Eggermont, J. J., Aertsen, A. M., & Johannesma, P. I. (1983). Quantitative characterisation procedure for auditory neurons based on the spectro-temporal receptive field. Hear. Res., 10(2), 167-190.
19. Eichhorn, J., Tolias, A., Zien, A., Kuss, M., Rasmussen, C., Weston, J., et al. (2004). Prediction on spike data using kernel algorithms. In S. Thr ̈un, L. K. Saul, & B. Scḧolkopf (Eds.), Advances in neural information processing systems, 16 (pp. 1367-1374). Cambridge, MA: MIT Press.
20. Eyherabide, H., Rokem, A., Herz, A., & Samengo, I. (2009). Bursts generate a nonreducible spike-pattern code. Front. Neurosci., 3(1), 8-14.
21. Faisal, A., Selen, L.,&Wolpert, D. (2008).Noise in the nervous system. Nature Reviews Neuroscience, 9(4), 292-303.
22. Gawne, T. J., Kjaer, T. W., & Richmond, B. J. (1996). Latency: Another potential code for feature binding in striate cortex. Journal of Neurophysiology, 76(2), 1356-1360.
23. Gawne, T. J., McClurkin, J. W., Richmond, B. J., & Optican, L. M. (1991). Lateral geniculate neurons in behaving primates. III. Response predictions of a channel model with multiple spatial-to-temporal filters. Journal of Neurophysiology, 66(3), 809-823.
24. Geisler, W. S., Albrecht, D. G., Salvi, R. J., & Saunders, S. S. (1991). Discrimination performance of single neurons: Rate and temporal-pattern information. Journal of Neurophysiology, 66(1), 334-362.
25. Gerstein, G., & Kiang, N. (1960). An approach to the quantitative analysis of electrophysiological data from single neurons. Biophysical Journal, 1(1), 15-28.
26. Gerstner, W. (2000). Population dynamics of spiking neurons: Fast transients, asynchronous states, and locking? Neural computation, 12, 43-89.
27. Gerstner,W. (2008). Spike-response model. Scholarpedia, 3(12), 1343.
28. Gerstner, W., & Kistler, W. (2002). Spiking neuron models. Cambridge: Cambridge University Press.
29. Gerstner,W., &Naud, R. (2009).How good are neuronmodels. Science, 326, 379-380.
30. Gerstner,W., vanHemmen, J.,&Cowan, J. (1996).Whatmatters in neuronal locking? Neural Computation, 8, 1653-1676.
31. Gollisch, T., & Meister, M. (2008). Rapid neural coding in the retina with relative spike latencies. Science, 319, 1108-1111.
32. Houghton,C. (2009). Studying spike trains using a van rossummetric with a synapselike filter. J. Comput. Neurosci., 26(1), 149-155.
33. Hunter, J. D., & Milton, J. G. (2003). Amplitude and frequency dependence of spike timing: implications for dynamic regulation. Journal of Neurophysiology, 90(1), 387-394.
34. Izhikevich, E., Desai, N., Walcott, E., & Hoppensteadt, F. (2003). Bursts as a unit of neural information: Selective communication via resonance. Trends in Neurosciences, 26(3), 161-167.
35. Jolivet, R., Kobayashi, R., Rauch, A., Naud, R., Shinomoto, S., & Gerstner,W. (2008). A benchmark test for a quantitative assessment of simple neuron models. Journal of Neuroscience Methods, 169, 417-424.
36. Jolivet, R., Rauch, A., L̈ uscher, H., & Gerstner, W. (2006). Predicting spike timing of neocortical pyramidal neurons by simple threshold models. Journal of Computational Neuroscience, 21, 35-49.
37. Kayser, C., Montemurro,M., Logothetis, N.,&Panzeri, S. (2009). Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. Neuron, 61(4), 597-608.
38. Kistler, W., Gerstner, W., & Hemmen, J. (1997). Reduction of the Hodgkin-Huxley equations to a single-variable threshold model. Neural Computation, 9, 1015-1045.
39. Kobayashi, R., Tsubo, Y., & Shinomoto, S. (2009). Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Frontiers in Computational Neuroscience, 3, 9.
40. Kreiman, G., Krahe, R., Metzner,W., Koch, C., & Gabbiani, F. (2000). Robustness and variability of neuronal coding by amplitude-sensitive afferents in the weakly electric fish eigenmannia. Journal of Neurophysiology, 84(1), 189-204.
41. Kreuz, T., Chicharro, D., Andrzejak, R. G., Haas, J. S., & Abarbanel, H.D.I. (2009). Measuring multiple spike train synchrony. J. Neurosci. Methods, 183(2), 287-299.
42. Kreuz, T., Haas, J., Morelli, A., Abarbanel, H., & Politi, A. (2007). Measuring spike train synchrony. Journal of Neuroscience Methods, 165(1), 151-161.
43. La Camera, G, Rauch, A., Thurbon, D., L̈ uscher, H., Senn, W., & Fusi, S. (2006). Multiple time scales of temporal response in pyramidal and fast spiking cortical neurons. Journal of Neurophysiology, 96, 3448-3464.
44. Lestienne, R. (1995). Determination of the precision of spike timing in the visual cortex of anaesthetised cats. Biological Cybernetics, 74(1), 55-61.
45. Lindner, B. (2006). Superposition of many independent spike trains is generally not a Poisson process. Phys. Rev. E, 73(2), 022,901.
46. Lisman, J. (1997). Bursts as a unit of neural information: Making unreliable synapses reliable. Trends in Neurosciences, 20(1), 38-43.
47. Lisman, J. (2005). The theta/gamma discrete phase code occurring during the hippocampal phase precession may be a more general brain coding scheme. Hippocampus, 15(7), 913-922.
48. Lundstrom, B., Higgs, M., Spain, W., & Fairhall, A. (2008). Fractional differentiation by neocortical pyramidal neurons. Nature Neuroscience, 11, 1335-1342.
49. MacPherson, J. M., & Aldridge, J. W. (1979). A quantitative method of computer analysis of spike train data collected from behaving animals. Brain Research, 175(1), 183-187.
50. Mainen, Z. F., & Sejnowski, T. J. (1995). Reliability of spike timing in neocortical neurons. Science, 268(5216), 1503-1506.
51. McCullagh, P., & Nelder, J. A. (1998). Generalized linear models (2nd ed.). London: Chapman & Hall/CRC.
52. Naud, R., Marcille, N., Clopath, C., & Gerstner, W. (2008). Firing patterns in the adaptive exponential integrate-and-firemodel. Biological Cybernetics, 99, 335-347.
53. Optican, L. M.,&Richmond, B. J. (1987). Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. 3. Information theoretic analysis. J. Neurophysiol., 57, 162-178.
54. Paiva, A.R.C, Park, I., & Príncipe, J. (2009a). A comparison of binless spike train measures. Neural Computing and Applications, 19(3), 1-15.
55. Paiva, A.R.C, Park, I., & Pŕ?ncipe, J. (2009b). A reproducing kernel Hilbert space framework for spike train signal processing. Neural Computation, 21(2), 424-449.
56. Paiva, A.R.C, Park, I., & Pŕ?ncipe, J. (2010). Inner products for representation and learning in the spike train domain. In K. G. Oweiss (Ed.), Statistical signal processing for neuroscience and neurotechnology. Orlando, FL: Academic Press.
57. Paninski, L., Pillow, J., & Simoncelli, E. (2005). Comparing integrate-and-firemodels estimated using intracellular and extracellular data. Neurocomputing, 65-66, 379- 385.
58. Panzeri, S., Senatore, R., Montemurro, M. A., & Petersen, R. S. (2007). Correcting for the sampling bias problem in spike train information measures. Journal of Neurophysiology, 98(3), 1064-1072.
59. Petersen, R. S., Brambilla,M., Bale,M. R., Alenda,A., Panzeri, S.,Montemurro,M.A., et al . (2008).Diverse and temporally precise kinetic feature selectivity in theVPM thalamic nucleus. Neuron, 60(5), 890-903.
60. Pillow, J., Paninski, L., Uzzell, V., Simoncelli, E., & Chichilnisky, E. (2005). Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. Journal of Neuroscience, 25(47), 11,003.
61. Pillow, J., Shlens, J., Paninski, L., Sher, A., Litke, A., Chichilnisky, E., et al. (2008). Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature, 454(7207), 995-999.
62. Plesser, H. E., & Gerstner,W. (2000). Escape rate models for noisy integrate-and-fire neurons. Neurocomputing, 32-33, 219-224.
63. Quiroga, R. Q., Kraskov, A., Kreuz, T., & Grassberger, P. (2002). Performance of different synchronization measures in real data: A case study on electroencephalographic signals. Phys. Rev. E, 65(4), 041903.
64. Quiroga, R. Q., Kreuz, T., & Grassberger, P. (2002). Event synchronization: A simple and fast method to measure synchronicity and time delay patterns. Phys. Rev. E, 66(4), 041904.
65. Reich, D.,Mechler, F., Purpura,K.,&Victor, J.D. (2000). Interspike intervals, receptive fields, and information encoding in primary visual cortex. Journal of Neuroscience, 20(5), 1964-1974.
66. Reich, D. S., Mechler, F., & Victor, J. D. (2001). Temporal coding of contrast in primary visual cortex:When, what, and why. Journal of Neurophysiology, 85(3), 1039-1050.
67. Reich, D. S., Victor, J. D., Knight, B. W., Ozaki, T., & Kaplan, E. (1997). Response variability and timing precision of neuronal spike trains in vivo. Journal of Neurophysiology, 77(5), 2836-2841.
68. Rieke, F., Warland, D., de Ruyter van Steveninck, R., & Bialek, W. (1996). Spikes: Exploring the neural code. Cambridge, MA: MIT Press.
69. Schoenberg, F. P., & Tranbarger, K. E. (2008). Description of earthquake aftershock sequences using prototype point patterns. Environmetrics, 19, 271-286.
70. Schrauwen, B., & Campenhout, J. (2007). Linking non-binned spike train kernels to several existing spike train metrics. Neurocomputing, 70(7-9), 1247-1253.
71. Schreiber, S., Fellous, J., Whitmer, D., Tiesinga, P., & Sejnowski, T. J. (2003). A new correlation-basedmeasure of spike timing reliability. Neurocomputing, 52(54), 925-931.
72. Shimazaki, H., & Shinomoto, S. (2007). A method for selecting the bin size of a time histogram. Neural Computation, 19(6), 1503-1527.
73. Shinomoto, S. (2010). Fitting a stochastic spiking model to neuronal current injection data. Neural Networks, 23(6), 764-769.
74. Shpigelman, L., Singer, Y., Paz, R., & Vaadia, E. (2005). Spikernels: Predicting arm movements by embedding population spike rate patterns in inner-product spaces. Neural Computation, 17(3), 671-690.
75. Snyder, D. L., Miller, M. I., & Snyder, D. L. (1991). Random point processes in time and space (2nd ed.). New York: Springer-Verlag.
76. Strong, S. P.,K̈oberle, R., de Ruyter van Steveninck,R. R.,&Bialek,W. (1998). Entropy and information in neural spike trains. Physical Review Letters, 80, 197-200.
77. Tiesinga, P.H. E. (2004). Chaos-induced modulation of reliability boosts output firing rate in downstream cortical areas. Physical Review E, 69(3 Pt. 1), 031912.
78. Tiesinga, P., Fellous, J., & Sejnowski, T. J. (2008). Regulation of spike timing in visual cortical circuits. Nature Reviews Neuroscience, 9(2), 97-109.
79. Treves, A., & Panzeri, S. (1995). The upward bias in measures of information derived from limited data samples. Neural Computation, 7, 399-407.
80. Truccolo, W., Eden, U., Fellows, M., Donoghue, J. P., & Brown, E. N. (2005). A point process framework for relating neural spiking activity to spiking history, neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. Journal of Neurophysiology, 93, 1074-1089.
81. Tsodyks,M., &Markram, H. (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc. Natl. Academy of Sci. USA, 94, 719-723.
82. van Rossum, M.C.W. (2001).Anovel spike distance. Neural Computation, 13, 751-763.
83. Victor, J. D. (2005). Spike train metrics. Current Opinion in Neurobiology, 15, 585-592.
84. Victor, J. D., & Purpura, K. (1996). Nature and precision of temporal coding in visual cortex: A metric-space analysis. Journal of Neurophysiology, 76(2), 1310-1326.
85. Victor, J. D., & Purpura, K. (1997). Metric-space analysis of spike trains: Theory, algorithms, and application. Network: Comput. Neural Syst., 8, 127-164.
86. Voinov, V. (1985). Unbiased estimation of powers of the inverse of mean and related problems. Sankhȳa: The Indian Journal of Statistics, Series B, 47(3), 354-364.
87. Voinov, V., & Nikulin, M. S. (1996). Unbiased estimators and their applications. Mathematics and its applications. Dordrecht: Kluwer.
88. Wang, L., Narayan, R., Grana, G., Shamir, M., & Sen, K. (2007). Cortical discrimination of complex natural stimuli: Can single neurons match behavior? Journal of Neuroscience, 27(3), 582-589.