Title :
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
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;
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
DOI :
10.1109/CNNA.2002.1035057
