A study of a neural-fuzzy network method for recognising change in neuronal discharge patterns

Abstract

A neural-fuzzy network was applied to detect changes in neuronal discharge patterns. The network was firstly trained by a set of input-output data to simulate the classification processing of neuronal discharge patterns assessed by experienced researchers. A second set of data was then used to test the network. Both train and test data were recorded from the cerebellar thalamus of a conscious monkey performing voluntary wrist movements. The preliminary result illustrates that the neural-fuzzy network has potential advantages in classification of neuronal discharge patterns.