Individual skill
neural-population-modeling
Population-level neural data modeling — dimensionality reduction, latent dynamics, and encoding/decoding models.
skills/neural-population-modeling/SKILL.md
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## Overview
neural-population-modeling treats simultaneously recorded neurons as a coordinated population rather
than a bag of single cells. It covers dimensionality reduction (PCA, GPFA, LFADS), latent dynamical
systems, encoding models (how stimuli drive activity), and decoding models (how activity predicts
behavior) — the toolkit behind modern systems neuroscience.
The central insight: population activity usually lives on a low-dimensional manifold, and the
geometry and dynamics of that manifold — not individual tuning curves — explain computation.
## When to use
- Reducing high-dimensional spike data: PCA, factor analysis, GPFA, LFADS, UMAP/t-SNE for
visualization.