  Neuronal responses to sensory stimuli or neuronal responses related to
behaviour are often extracted by averaging neuronal activity over large number
of experimental trials. Such trial-averaging is carried out to reduce noise and
to reduce the influence of other signals unrelated to the corresponding
stimulus or behaviour. However, if the recorded neuronal responses are jittered
in time with respect to the corresponding stimulus or behaviour, averaging over
trials may distort the estimation of the underlying neuronal response. Here, we
present an algorithm, named dTAV algorithm, for realigning the recorded
neuronal activity to an arbitrary internal trigger. Using simulated data, we
show that the dTAV algorithm can reduce the jitter of neuronal responses for
signal to noise ratios of 0.2 or higher, i.e. in cases where the standard
deviation of the noise is up to five times larger than the neuronal response
amplitude. By removing the jitter and, therefore, enabling more accurate
estimation of neuronal responses, the dTAV algorithm can improve analysis and
interpretation of the responses and improve the accuracy of systems relaying on
asynchronous detection of events from neuronal recordings.
