Description Usage Arguments Details Value Author(s) References Examples
The most commonly used and intensively studied metrics for spike trains, which is based on the continuation of event sequence to a real valued continuous function using a smoother function.
1 2 |
S1 |
marked point process data. |
S2 |
marked point process data. |
measure |
|
h |
filtering function. Default |
tau |
parameter for filtering function. |
M |
a precision matrix for filter of marks, i.e., exp( - r' M r) is used for filtering marks. It should be symmetric and positive semi-definite. |
abs.tol |
absolute tolerance for numerical integration. |
fmetric
computes filter-based measure between MPP realizations. Discrete event timings are transformed into a continuous function by using a kernel smoother, and usual l2 inner product is adopted for defining the similarity between two point process realizations.
Similarity or distance between two inputs (marked) point process S1 and S2.
Hideitsu Hino hinohide@cs.tsukuba.ac.jp, Ken Takano, Yuki Yoshikawa, and Noboru Murata
M. C. W. van Rossum. A Novel Spike Distance. Neural Computation, Vol. 13(4), pp. 751-763, 2001.
S. Schreiber, J.M. Fellous, P.H. Tiesinga, and T.J. Sejnowski. A new correlation-based measure of spike timing reliability, Neurocomputing, Vols. 52-54, pp. 925-931, 2003.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ##The aftershock data of 26th July 2003 earthquake of M6.2 at the northern Miyagi-Ken Japan.
data(Miyagi20030626)
## time longitude latitude depth magnitude
## split events by 7-hour
sMiyagi <- splitMPP(Miyagi20030626,h=60*60*7,scaleMarks=TRUE)$S
N <- 10
tau <- 0.1
sMat <- matrix(0,N,N)
cat("calculating fmetric with tau ",tau,"...")
for(i in 1:(N)){
cat(i," ")
for(j in i:N){
S1 <- sMiyagi[[i]]$time;S2 <- sMiyagi[[j]]$time
sMat[i,j] <- fmetric(S1,S2,tau=tau,M=diag(1,4))
}
}
sMat <- sMat+t(sMat)
tmpd <- diag(sMat) <- diag(sMat)/2
sMat <- sMat/sqrt(outer(tmpd,tmpd))
image(sMat)
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