288 pp. per issue
6 x 9, illustrated
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Neural Computation

January 1, 1997, Vol. 9, No. 1, Pages 51-76
(doi: 10.1162/neco.1997.9.1.51)
© 1997 Massachusetts Institute of Technology
Detecting Synchronous Cell Assemblies with Limited Data and Overlapping Assemblies
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Two statistical methods—cross-correlation (Moore et al. 1966) and gravity clustering (Gerstein et al. 1985)—were evaluated for their ability to detect synchronous cell assemblies from simulated spike train data. The two methods were first analyzed for their temporal sensitivity to synchronous cell assemblies. The presented approach places a lower bound on the amount of data required to detect a synchronous assembly. On average, both methods required the same minimum amount of recording time to detect significant pairwise correlations, but the gravity method exhibited less variance in the recording time. The precise length of recording depends on the consistency with which a neuron fires synchronously with the assembly but was independent of the assembly firing rate. Next, the statistical methods were tested with respect to their ability to differentiate two distinct assemblies that overlapped in time and space. Both statistics could adequately differentiate two overlapping synchronous assemblies. For cross-correlation, this ability deteriorates quickly when considering three or more simultaneously active, overlapping assemblies, whereas the gravity method should be more flexible in this regard. The work demonstrates the difficulty of detecting assembly phenomena from simultaneous neuronal recordings. Other statistical methods and the detection of other types of assemblies are also discussed.