TITLE: 
An efficient method for detecting connectivity in neural ensembles.
AUTHORS: 
Edwards BW; Wakefield GH
AUTHOR AFFILIATION: 
Department of Electrical Engineering and Computer Science, University of
Michigan, Ann Arbor 48109-2122.
SOURCE: 
J Neurosci Methods 1992 Oct-Nov;45(1-2):1-14
CITATION IDS: 
PMID: 1337133 UI: 93148645
ABSTRACT: 
Modern technology is allowing researchers to collect data from neural
ensembles with a large number of units, and the analysis of interaction
between these units can be very time consuming. Estimation of pairwise
connectivity is the most common method of determining the neural
'network' but usually necessitates the production of numerous histograms
for each pair considered. We present a method which will indicate which
pairs in a network represent potential connections and thereby simplify the
postexperimental analysis. The technique uses cross-interval information to
create an n x n matrix which represents all possible connections in an n
neuron ensemble and can be calculated recursively on-line. The
performance of this technique is analyzed with respect to data size and
strength of the connections. It is compared to 2 similar techniques that are
also presented here, one in which perfect knowledge of the timing of the
excitation is known, and one in which the timing can be bounded.
MAIN MESH HEADINGS: 
*Computer Simulation
*Models, Neurological
*Neural Networks (Computer)
Neural Pathways/*physiology