Analyzing multidimensional neural activity via CNN-UM

Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review

Contributors

  • V. Gál - , University Hospital Frankfurt (Author)
  • S. Grün - , Max Planck Institute for Brain Research (Author)
  • R. Tetzlaff - , University Hospital Frankfurt (Author)

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.

Details

Original languageEnglish
Title of host publicationProceedings of the 7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002
EditorsRonald Tetzlaff
PublisherIEEE, New York [u. a.]
Pages243-250
Number of pages8
ISBN (electronic)981238121X
Publication statusPublished - 2002
Peer-reviewedYes
Externally publishedYes

Workshop

Title7th IEEE International Workshop on Cellular Neural Networks and their Applications
Abbreviated titleCNNA 2002
Conference number7
Duration22 - 24 July 2002
Degree of recognitionInternational event
CityFrankfurt am Main
CountryGermany

External IDs

ORCID /0000-0001-7436-0103/work/142240271

Keywords

ASJC Scopus subject areas

Keywords

  • Cellular neural networks, Electrodes, Electrophysiology, Frequency synchronization, Multidimensional systems, Neurons, Neurophysiology, Physics, Signal analysis, Time series analysis