Assembly Detection in Continuous Neural Spike Train Data
| Type of publication: | Incollection |
| Citation: | |
| Booktitle: | Advances in Intelligent Data Analysis XI |
| Series: | Lecture Notes in Computer Science |
| Volume: | 7619 |
| Year: | 2012 |
| Pages: | 78-89 |
| Publisher: | Springer Berlin / Heidelberg |
| Note: | 10.1007/978-3-642-34156-4_9 |
| ISBN: | 978-3-642-34155-7 |
| URL: | http://dx.doi.org/10.1007/978-... |
| DOI: | 10.1007/978-3-642-34156-4_9 |
| Abstract: | 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. |
| Keywords: | continuous data, ensemble detection, Hebbian learning, multidimensional scaling , spike train |
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| Added by: | [] |
| Total mark: | 0 |
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