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Removing nonlinear misalignment in neuronal spike trains using the Fisher-Rao registration framework
Zishen Xu1, Xinyu Zhou1, Yiqi Xu1
1Department of Statistics, Florida State University, 117 N Woodward Ave., Tallahassee, FL 32306-4330, USA.
Journal of Neuroscience Methods
|December 10, 2021
Summary
The Fisher-Rao Registration (FRR) method effectively aligns noisy neural spike train data. This nonlinear approach improves understanding of temporal variability in neural activity.
Area of Science:
- Computational Neuroscience
- Neuroscience
- Data Analysis
Background:
- Temporal precision in neural spike trains is crucial for understanding nervous system function.
- Spiking activity timing variability can be highly nonlinear due to behavioral or cognitive factors.
Purpose of the Study:
- To introduce the Fisher-Rao Registration (FRR) as a nonlinear method for analyzing neural spike trains.
- To address and remove nonlinear phase variability in discrete neuronal spike train data.
Main Methods:
- Developed a smoothing procedure for discrete spike train data to enable FRR application.
- Applied the Fisher-Rao Registration (FRR), a powerful nonlinear technique.
- Systematically compared FRR against existing linear and nonlinear methods for efficiency and effectiveness.
Main Results:
- FRR demonstrated superior performance in aligning neuronal spike trains.
- Simulation and real experimental data confirmed the advantages of the FRR method.
Conclusions:
- The FRR framework offers superior alignment performance for neuronal spike trains.
- FRR provides a more appropriate method for understanding temporal variability in neural activity.
