Warning
This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!
correlation_detector – Device for evaluating cross correlation between two spike sources¶
Description¶
The correlation_detector device is a recording device. It is used to record spikes from two pools of spike inputs and calculates the count_histogram of interspike intervals (raw cross correlation) binned to bins of duration \(\delta_\tau\). The result can be obtained via GetStatus under the key /count_histogram. In parallel it records a weighted histogram, where the connection weights are used to weight every count. In order to minimize numerical errors, the Kahan summation algorithm is used when calculating the weighted histogram. Both are arrays of \(2*\tau_{max}/\delta_{\tau}+1\) values containing the histogram counts in the following way:
Let \(t_{1,i}\) be the spike times of source 1, \(t_{2,j}\) the spike times of source 2. histogram[n] then contains the sum of products of the weight \(w_{1,i}*w_{2,j}\), count_histogram[n] contains 1 summed over all events with \(t_{2,j}t_{1,i}\) in
The bins are centered around the time difference they represent, but are leftclosed and rightopen. This means that events with time difference tau_maxdelta_tau/2 are counted in the leftmost bin, but event with difference tau_max+delta_tau/2 are not counted at all.
The correlation detector has two inputs, which are selected via the receptor_port of the incoming connection: All incoming connections with receptor_port = 0 will be pooled as the spike source 1, the ones with receptor_port = 1 will be used as spike source 2.
Parameters¶
Tstart 
real 
Time when to start counting events. This time 
should 
be set to at least start + tau_max in order to 

avoid 
edge effects of the correlation counts. 

Tstop 
real 
Time when to stop counting events. This time 
should 
be set to at most Tsim  tau_max, where Tsim is 

the 
duration of simulation, in order to avoid edge effects of the correlation counts. 

delta_tau 
ms 
Bin width. This has to be an odd multiple of the resolution, to allow the symmetry between positive and negative timelags. 
tau_max 
ms 
Onesided width. In the lower triagnular part events with differences in [0, 
tau_max+delta_tau/2) 
are counted. On the diagonal and in the upper triangular part events with differences in (0, tau_max+delta_tau/2]. 

N_channels 
integer 
The number of pools. This defines the range of receptor_type. Default is 1. Setting N_channels clears count_covariance, covariance and n_events. 
histogram 
squared 
readonly  raw, weighted, crosscorrelation 
counts 
synaptic weights 
Unit depends on model 
histogram_correction 
list of integers 
readonly  Correction factors for Kahan summation algoritm 
n_events 
list of integers 
Number of events from source 0 and 1. By setting n_events to [0,0], the histogram is cleared. 
Remarks:
This recorder does not record to file, screen or memory in the usual sense.
Correlation detectors IGNORE any connection delays.
Correlation detector breaks with the persistence scheme as follows: the internal buffers for storing spikes are part of State_, but are initialized by init_buffers_().
@todo The correlation detector could be made more efficient as follows (HEP 20080701):  incoming_ is vector of two deques  let handle() push_back() entries in incoming_ and do nothing else  keep index to last “old spike” in each incoming_; cannot
be iterator since that may change
update() deletes all entries before nowtau_max, sorts the new entries, then registers new entries in histogram
Example:
See Auto and crosscorrelation functions for spike trains[cross_check_mip_corrdet.py] in pynest/examples.
Receives¶
SpikeEvent