Title :
An artificial neural network for neural spike classification
Author :
Stitt, J.P. ; Gaumond, R.P. ; Frazier, J.L. ; Hanson, F.E.
Author_Institution :
Pennsylvania State Univ., University Park, PA, USA
Abstract :
In insects, the summed responses of neural activity can be obtained by recording from the exterior of a taste organ (sensillum) of an intact animal. These multiunit recordings are commonly used to understand sensory and behavioral physiology. It is possible to distinguish between the neural spikes produced by these chemosensory neurons using such features as amplitude and shape. We have developed an artificial neural network (ANN) spike classifier which is capable of distinguishing among neural responses of each insect taste organ. The ANN is "trained" on prototypical spikes produced by each of the constituent neurons. It performs very well when compared with conventional optimal methods of template matching and principal components
Keywords :
backpropagation; biology computing; chemioception; feature extraction; feedforward neural nets; neurophysiology; pattern classification; zoology; amplitude; artificial neural network; behavioral physiology; chemosensory neurons; insects; intact animal; mixed spike train; multiunit recordings; neural activity; neural spike classification; sensillum; sensory physiology; shape; taste organ; Animals; Artificial neural networks; Chemical sensors; Frequency; Information analysis; Insects; Neurons; Optical fiber sensors; Prototypes; Sorting;
Conference_Titel :
Bioengineering Conference, 1997., Proceedings of the IEEE 1997 23rd Northeast
Conference_Location :
Durham, NH
Print_ISBN :
0-7803-3848-0
DOI :
10.1109/NEBC.1997.594936