CoCoNAD (Continuous-time Closed Neuron Assembly Detection) is a method to find frequent 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.
|Languages used||C, Matlab|
|Supported OSs||64-bit Linux|
||continuous-time closed neuron assembly detection|
||estimate a pattern spectrum from original data|
||generate a pattern spectrum from surrogate data|
||pattern set reduction based on a preference relation|
||export patterns to text file|
||extract a decision border from a pattern spectrum|
||read spike trains from file|
Picado-Muiño D, Borgelt C (2014).
Frequent item set mining for sequential data: synchrony in neuronal spike trains.
Intelligent Data Analysis 18(6):997-1012.
Borgelt C, Picado-Muiño D (2013).
Finding frequent patterns in parallel point processes.
Proc. 12th Int. Symposium on Intelligent Data Analysis, 116-126.
Picado-Muiño D, Borgelt C, Berger D, Gerstein G, Grün S (2013).
Finding neural assemblies with frequent item set mining.
Frontiers in Neuroinformatics 7:9.
Torre E, Picado-Muiño D, Denker M, Borgelt C, Grün S (2013).
Statistical evaluation of synchronous spike patterns extracted by frequent itemset mining.
Frontiers in Computational Neuroscience, 7:132.
Borgelt C, Picado-Muiño D (2014).
Simple pattern spectrum estimation for fast pattern filtering with CoCoNAD.
Proc. 13th Int. Symposium on Intelligent Data Analysis, 37-48.