Removing nonlinear misalignment in neuronal spike trains using the Fisher-Rao registration framework.

BACKGROUND

The temporal precision in neural spike train data is critically important for understanding functional mechanism in the nervous systems. However, the timing variability of spiking activity can be highly nonlinear in practical observations due to behavioral variability or unobserved/unobservable cognitive states.

NEW METHOD

In this study, we propose to adopt a powerful nonlinear method, referred to as the Fisher-Rao Registration (FRR), to remove such nonlinear phase variability in discrete neuronal spike trains. We also develop a smoothing procedure on the discrete spike train data in order to use the FRR framework.

COMPARISON WITH EXISTING METHODS

We systematically compare the FRR with the state-of-the-art linear and nonlinear methods in terms of model efficiency and effectiveness.

RESULTS

We show that the FRR has superior performance and the advantages are well illustrated with simulation and real experimental data.

CONCLUSIONS

It is found the FRR framework provides more appropriate alignment performance to understand the temporal variability in neuronal spike trains.