Inference based method for realignment of single trial neuronal responses

TL;DR

This paper introduces the dTAV algorithm, which realigns neuronal responses in single trials to reduce jitter, thereby improving the accuracy of neuronal response estimation and analysis in noisy conditions.

Contribution

The paper presents a novel realignment algorithm, dTAV, capable of reducing neuronal response jitter in single-trial data, enhancing analysis accuracy especially under high noise conditions.

Findings

dTAV reduces jitter for SNR ≥ 0.2

Improves estimation of neuronal responses

Enhances accuracy of event detection from neuronal data

Abstract

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.