Title :
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
fDate :
30 Oct-2 Nov 1997
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;
Conference_Titel :
Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-4262-3
DOI :
10.1109/IEMBS.1997.756540
