Analyzing multidimensional neural activity via CNN-UM

Author

Gál, Viktor ; Grün, Sonja ; Tetzlaff, Ronald

Author_Institution

Inst. for Appl. Phys., Univ. of Frankfurt, Frankfurt/Main, Germany

fYear

2002

fDate

22-24 Jul 2002

Firstpage

243

Lastpage

250

Abstract

In this paper we show that CNN-UM is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: the occurrences of different patterns are first counted, then the statistical significance of each occurrence frequency is calculated separately.

Keywords

bioelectric potentials; cellular neural nets; medical signal processing; neurophysiology; pattern classification; statistical analysis; time series; CNN-UM; cellular neural nets; electrophysiological multineuron recordings; multidimensional activity patterns; multidimensional binary signals; pattern classification; spike activity; statistical analysis; time series; Cellular neural networks; Electrodes; Electrophysiology; Frequency synchronization; Multidimensional systems; Neurons; Neurophysiology; Physics; Signal analysis; Time series analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Cellular Neural Networks and Their Applications, 2002. (CNNA 2002). Proceedings of the 2002 7th IEEE International Workshop on

Print_ISBN

981-238-121-X

Type

conf

DOI

10.1109/CNNA.2002.1035057

Filename

1035057