Beyond correlation: optimal transport metrics for characterizing representational stability and remapping in neurons...

Area of Science:

• Neuroscience
• Computational Neuroscience

Background:

• Spatial representations in the entorhinal cortex (EC) and hippocampus (HPC) are crucial for navigation and memory.
• These representations remap dynamically with environmental changes, but current quantification methods are limited.

Purpose of the Study:

• To propose a novel framework using Earth Mover's Distance (EMD) for characterizing neural remapping events.
• To overcome limitations of traditional methods in capturing complex or non-linear remapping.

Main Methods:

• Developed a family of metrics based on the Earth Mover's Distance (EMD).
• Applied EMD to analyze spatial field maps in the EC and HPC.

Main Results:

• EMD provides a granular, noise-resistant, and rate-robust description of remapping.
• This method facilitates identification of specific cell types and characterization of remapping in various conditions, including disease models.
• EMD can be adapted to identify spatially tuned cells and analyze remapping with other information forms like spatiotemporal coding.

Conclusions:

• The EMD offers a feasible and lightweight approach to complement existing remapping analysis techniques.
• EMD holds significant potential for advancing the understanding of neural remapping in spatial navigation and memory.