Maintaining knowledge about temporal intervals
Communications of the ACM
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Efficient identification of assembly neurons within massively parallel spike trains
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Finding ensembles of neurons in spike trains by non-linear mapping and statistical testing
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
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Since Hebb's work on the organization of the brain [16] finding cell assemblies in neural spike trains has become a vivid field of research. As modern multi-electrode techniques allow to record the electrical potentials of many neurons in parallel, there is an increasing need for efficient and reliable algorithms to identify assemblies as expressed by synchronous spiking activity. We present a method that is able to cope with two core challenges of this complex task: temporal imprecision (spikes are not perfectly aligned across the spike trains) and selective participation (neurons in an ensemble do not all contribute a spike to all synchronous spiking events). Our approach is based on modeling spikes by influence regions of a user-specified width around the exact spike times and a clustering-like grouping of similar spike trains.