The local variation of inter-spike intervals Lv Version 1.7: Sept, 15^th 2010

This program was created by Nanae Matsuno in collaboration with Shigeru Shinomoto based on the study published in
S. Shinomoto, K. Shima and J. Tanji, Neural Computation, 15 (2003) 2823-2842.
S. Shinomoto, H. Kim, T. Shimokawa, N. Matsuno, S. Funahashi, et.al., PLoS Computational Biology (2009) 5:e1000433.
In order to run this program, you have to install Java (Free download site: http://www.java.com/en/ ).

1. Copy-and-paste a spike sequence of a single neuron.

In this application, the spike times should be arranged in ascending order; T_i<T_i+1.

24 26 32 47 52 55 57 60 62 64 67 69 72 75 77 79 81 83 86 88 92 96 104 107 110 112 120 122 125 129 141 143 146 148 151 153 155 169 171 172 179 181 184 186 195 197 203 205 209 213 217 219 222 230 233 245 250 252 254 261 263 266 269 271 279 280 283 303 305 323 324 326 329 332 338 343 358 361 365 372 374 378 387 389 403 405 407 413 415 417 420 443 445 448 458 461 464 466 469 472 510 512 529 532 534 537 540 542 545 547 549 551 553 590 592 595 597 601 608 622 625 635 641 799 801 803 805 807 810 812 814 816 818 821 823 825 827 829 831 834 838 844 847 849 852 855 857 861 863 865 869 874 876 882 887 894 897 899 904 906 909 911 915 918 923 925 927 929 932 934 938 941 959 960 962 966 972 974 979 983 986 988 992 994 997 999 1001 1019 1021 1023 1026 1030 1032 1036 1038 1040 1043 1046 1074 1076 1080 1082 1085 1087 1090 1094 1096 1114 1116 1118 1120 1127 1129 1131 1134 1136 1143 1146 1149 1151 1153 1156 1164 1166 1169 1185 1206 1208 1212 1222 1224 1249 1251 1254 1274 1276 1280 1282 1288 1290 1292 1294 1297 1300 1306 1308 1310 1312 1375 1377 1379 1381 1383 1387 1408 1410 1413 1415 1428 1429 1434 1436 1438 1445 1448 1492 1494 1497 1500 1503 1505 1507 1514 1526 1528 1530 1532 1535 1537 1539 1541 1543 1548 1550 1554 1556 1558 1561 1563 1567 1569 1573 1578 1581 1584 1588 1590 1592 1594 1596 1599 1600 1603 1605 1608 1611 1613 1618 1623 1625 1631 1633 1643 1645 1647 1650 1652 1659 1661 1664 1669 1674 1679 1684 1702 1704 1707 1725 1727 1729 1732 1734 1736 1751 1753 1758 1760 1763 1767 1772 1774 1777 1787 1791 1793 1795 1798 1800 1808 1810 1812 1817 1820 1822 1827 1867 1869 1870 1875 1878 1880 1885 1889 1891 1893 1898 1900 1903 1906 1938 1940 1942 1945 1948 1950 1953 1958 1960 1962 1965 1967 1970 1972 1975 1980 1982 1984 1987
The number of ISIs segmented: n =

2. Compute metrics Lv, Cv and Log₁₀(T)

3. Output sheet

The values of Lv, Cv, and Log₁₀(T) are computed for each segmented sequence of n+1 spikes, whose time is represented by a median.

REFERENCES

Differences in spiking patterns among cortical neurons
S. Shinomoto, K. Shima and J. Tanji, Neural Computation, 15 (2003) 2823-2842. [PDF]

Regional and laminar differences in in vivo firing patterns of primate cortical neurons
S. Shinomoto, Y. Miyazaki, H. Tamura and I. Fujita, J. Neurophysiol. 94 (2005) 567-575. [PDF]

Empirical Bayes interpretations of random point events
S. Koyama and S. Shinomoto, J. Phys. A 38 (2005) L531-L537

Estimating instantaneous irregularity of neuronal firing
T. Shimokawa and S. Shinomoto, Neural Computation (2009) 21:1931-1951.

Relating neuronal firing patterns to functional differentiation of cerebral cortex
S. Shinomoto, H. Kim, T. Shimokawa, N. Matsuno, S. Funahashi, K. Shima, I. Fujita, H. Tamura, T. Doi, K. Kawano, N. Inaba, K. Fukushima, S. Kurkin, K. Kurata, M. Taira, K. Tsutsui, H. Komatsu, T. Ogawa, K. Koida, J. Tanji, and K. Toyama,
PLoS Computational Biology (2009) 5:e1000433. [open access]

A Non-universal Aspect in the Temporal Occurrence of Earthquakes.
X. Zhao, T. Omi, N. Matsuno, and S. Shinomoto,
New Journal of Physics (2010) 12:063010. [open access]

Toolbox for analyzing spike data, SULAB