A COMPARISON OF METHODS USED TO DETECT CHANGES IN NEURONAL DISCHARGE PATTERNS

The discharge pattern of two thalamic neurones was recorded from a con scious monkey performing voluntary movements about the wrist joint. Th e neuronal discharge was displayed as a raster centred on movement of the wrist. The discharge patterns of both neurones was very strongly c orrelated with movement. Three experienced researchers were asked to e xamine the data and to classify every part of each trial as background discharge, 'on' (increased firing rate) or 'off' (decreased or zero f iring rate) and to mark the times that neuronal discharge changed slat e. A 'standard output' was made from these classifications. A back-pro pagation artificial Neural Network (the Network) was used to model the standard output and cumulative sums (CUSUMs) and maximum likelihood w as then performed on the data and compared with the Network. There was a high correlation between the output of each observer (r > 0.61) and the standard output and between the Network and the standard output ( r > 0.99). However the correlation between standard output and CUSUMs (r = 0.06) and standard output and maximum likelihood (r = 0.36) was m uch lower. The Network could be trained with as few as 12 trials, indi cating a high degree of constancy in the methods employed by the obser vers. The Network was also highly efficient at detecting changes in st ate of neuronal activity (r > 0.99). In summary, when used on single t rial data, visual inspection is a reliable method for detecting timing of change neuronal discharge and is superior to CUSUM and maximum lik elihood. As well it is capable of detecting neuronal discharge state: that is whether firing rate is increased, normal or decreased. Neural Networks promise to be a useful method of confirming the consistency o f visual inspection as a means of detecting changes in neuronal discha rge pattern. (C) 1997 Elsevier Science B.V.