Neural Population Analysis Guide

What problem does it solve?

This guide helps researchers identify the dimensionality and latent structure of neural population activity, enabling principled selection among population analysis methods.

Core Features & Use Cases

• Method guidance: PCA, GPFA, dPCA, jPCA, and related approaches for population data.
• Best-practice preprocessing: Soft normalization and variance-stabilizing transforms for reliable dimensionality estimates.
• Decision-support: Demix and visualize neural variance by task parameters (stimulus, decision, time) to inform experimental design and analysis strategy.
• Use Case: Analyze simultaneous neural recordings to extract low-dimensional trajectories and assess the dominance of task parameters.

Quick Start

Load neural population data and start a dimensionality-reduction analysis following this guide.