Computational NeuroscienceMeasuring adaptation with a sinusoidal perturbation function

We examine the possibility that sensory and motor adaptation may be induced via a sinusoidally incremented perturbation. This sinewave adaptation method provides superior data for fitting a parametric model than when using the standard step-function method of perturbation, due to the relative difficulty of fitting a decaying exponential vs. a sinusoid. Using both experimental data and simulations, we demonstrate the difficulty of detecting the presence of motor adaptation using a step-function perturbation, compared to detecting motor adaptation using our sinewave perturbation method.

► We identify two potential problems with step-function adaptation paradigms. ► Large perturbations are often required to produce detectable results, but can lead to cognitive responses. ► Much of the data is collected during baseline or at steady-state responding. ► We present an adaptation paradigm using incremental perturbations following a sinusoidal pattern. ► Simulation and experimental data show that the sinewave adaptation paradigm remediates both issues.