TY  - CHAP
T1  - Assembly Detection in Continuous Neural Spike Train Data
A1  - Braune, Christian
A1  - Borgelt, Christian
A1  - Grün, Sonja
ED  - Hollmén, Jaakko
ED  - Klawonn, Frank
ED  - Tucker, Allan
TI  - Advances in Intelligent Data Analysis XI
T3  - Lecture Notes in Computer Science
Y1  - 2012
VL  - 7619
SP  - 78
EP  - 89
PB  - Springer Berlin / Heidelberg
SN  - 978-3-642-34155-7
N1  - 10.1007/978-3-642-34156-4_9
UR  - http://dx.doi.org/10.1007/978-3-642-34156-4_9
M2  - doi: 10.1007/978-3-642-34156-4_9
KW  - continuous data
KW  - ensemble detection
KW  - Hebbian learning
KW  - multidimensional scaling
KW  - spike train
N2  - 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.
ER  -