A comparison of neural spike classification techniques [caterpillar taste organs application]

Author

Stitt, J.P. ; Gaumond, R.P. ; Frazier, J.L. ; Hanson, F.E.

Author_Institution

Pennsylvania State Univ., University Park, PA, USA

Volume

3

fYear

1997

fDate

30 Oct-2 Nov 1997

Firstpage

1092

Abstract

This paper presents an Artificial Neural Network (ANN) capable of sorting neural spikes contained in a single-channel multiunit recording. The ANN performs very well when compared with Template Matching and Principal Components, two of the conventional optimal spike classification methods that have been widely used for sorting action potentials

Keywords

bioelectric potentials; biological techniques; biology computing; chemioception; neural nets; neurophysiology; signal processing; action potentials sorting; artificial neural network; caterpillar taste organs; electrophysiological recordings; neural spike classification techniques; principal components; sensory neurons; single-channel multiunit recording; template matching; Artificial neural networks; Chemical analysis; Chemical compounds; Frequency modulation; Neurons; Principal component analysis; Pulse modulation; Shape; Sorting; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE

Conference_Location

Chicago, IL

ISSN

1094-687X

Print_ISBN

0-7803-4262-3

Type

conf

DOI

10.1109/IEMBS.1997.756540

Filename

756540