Abstract
This paper describes the development of a cross platform crosscorrelation and clustering application for neural spike train recordings that will scale seamlessly from use on a researchers PC to a high performance computing cluster (HPC). Clusters of neurons are identified from the neural recordings using a hierarchical agglomerative clustering algorithm applied to the cross correlation data. Finally a cross correlation grid is used to visualise the neural clusters with the user navigating through the dataset using a dendrogram depicting the identified clusters. The cross correlation algorithm is an “embarrassingly parallel problem” that is scaled to cope with a large number of neurons through exploiting multiple compute cores both in the setting of a researchers PC or an HPC. This scaling is achieved using MPJ Expressa Java implementation of the Message Passing Interface (MPI).
Publication Date
2014-05-01
Publication Title
International Journal of Computer Application
Publisher
Foundation of Computer Science
ISSN
2250-1797
Embargo Period
2024-11-22
Recommended Citation
Stuart, L., Tucker, R., Barlow, N., & Gunaratne, S. (2014) 'A scaling cross platform tool for the analysis of neurophysiological data', International Journal of Computer Application, . Foundation of Computer Science: Retrieved from https://pearl.plymouth.ac.uk/secam-research/1863
Comments
See http://rspublication.com/ijca/ijca_index.htm