Neural Population Analysis Guide

Purpose

This skill encodes expert methodological knowledge for dimensionality reduction and latent-variable analysis of neural population recordings. A competent programmer without computational neuroscience training will get this wrong because:

• Not all dimensionality reduction is the same. PCA, Factor Analysis, GPFA, and dPCA answer fundamentally different questions about neural data. PCA finds axes of maximum variance; dPCA demixes variance by task parameter; GPFA extracts smooth single-trial trajectories. Choosing the wrong method answers the wrong question (Cunningham & Yu, 2014).
• Standard data science preprocessing destroys neural signal. Naive z-scoring or standard scaling of neural firing rates removes important information about rate differences across neurons. Soft normalization is required (Churchland et al., 2012).
• Visualization methods are not analysis methods. t-SNE and UMAP produce visually compelling low-dimensional embeddings but their distances are not interpretable, axes are not meaningful, and results are sensitive to hyperparameters. They must never be used for quantitative inference (Cunningham & Yu, 2014).
• Dimensionality is not determined by "percent variance explained." There is no universal threshold (e.g., 90%) for choosing the number of PCs. Parallel analysis or cross-validation is required to determine true dimensionality (Humphries, 2021).

When to Use This Skill

• Analyzing simultaneously recorded neural populations (multi-electrode arrays, Neuropixels, calcium imaging)
• Choosing between PCA, Factor Analysis, GPFA, dPCA, jPCA, or nonlinear methods
• Extracting low-dimensional neural trajectories from spike trains
• Demixing neural variance by task parameters (stimulus, decision, time)
• Setting up population decoding analyses with temporal generalization
• Determining data requirements (neuron counts, trial counts) for population analyses