CoCoNAD (Continuous-time Closed Neuron Assembly Detection) is a program to find frequent imprecisely synchronous joint events in parallel point processes, which has applications in the analysis of parallel spike trains. The idea is to provide a method to test the temporal coincidence coding hypothesis, that is, that stimuli are encoded by temporally coincident spiking of groups of neurons, sometimes called cell assemblies. The Python implementation is much slower (by about a factor of 40), but supports a graded notion of synchrony. This is (currently) not supported by the C implementation, which is restricted to a binary notion of synchrony (that is, a set of events is either synchronous or not). However, if a binary notion of synchrony is acceptable and the algorithm is to be used in Python for actual mining tasks, it is recommended to employ the PyCoCo extension module, which provides a Python interface to the C implementation.

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References in zbMATH (referenced in 3 articles )

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  1. Quaglio, Pietro; Rostami, Vahid; Torre, Emiliano; Grün, Sonja: Methods for identification of spike patterns in massively parallel spike trains (2018)
  2. Wood, Cynthia I.; Hicks, Illya V.: The minimal (k)-core problem for modeling (k)-assemblies (2015)
  3. Borgelt, Christian; Picado-Muiño, David: Simple pattern spectrum estimation for fast pattern filtering with coconad (2014) ioport