Authors:
Ninah Koolen
;
Ivan Gligorijevic
and
Sabine Van Huffel
Affiliation:
Katholieke Universiteit Leuven, Belgium
Keyword(s):
Neural Activity, Spikes, Spike Clustering, Statistical Parameters.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
Abstract:
In order to investigate functioning of the brain processes, it is important to have reliable processing of neural activity. For precise tracking of local neural network processes, reliable clustering of single neurons’ action potentials (spikes) is necessary. So far, it was common to keep the signals of high quality and discard the others. This work examines the possibility of extracting reliable information from bad quality signals, in the presence of spike classification errors. We tested the robustness and information capacity of several statistical parameters used to describe firing patterns of spike trains using simulated signals mimicking most common cases in nature. Although complete reconstruction of firing patterns is not always possible, we show that the approximation of the mean firing frequency as well as the detection of bursting processes can still be quantified successfully, thereby paving the way for future applications.