Fast spike pattern detection using the correlation integral.
Conventional approaches to detect patterns in neuronal firing are template based. As the pattern length increases, the number of trial patterns to be tested leads to strongly divergent computational costs. To remedy this problem, we propose a different statistical approach, based on the correlation integral. Applications of our method to model and neuronal data demonstrate its reliability, even in the presence of noise. Additionally, our investigation provides interesting insights into the nature of correlation-integral anomalies.
Institute of Neuroinformatics, University / ETH Zürich, Winterthurerstrasse 190, 8057 Zürich, Switzerland.
M
M
Christen
A
A
Kern
A
A
Nikitchenko
W-H
WH
Steeb
R
R
Stoop
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D002415:Cats; D003198:Computer Simulation; D016247:Information Storage and Retrieval / Q000379:methods; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated; D013223:Statistics as Topic; D013269:Stochastic Processes; D009435:Synaptic Transmission / Q000502:physiology*