Decodanda

Decoding and geometrical analysis of neural activity with built-in best practices

Github

Decoding neural activity is a standard practice in neuroscience, often used to claim that a particular population represents a specific variable. Unfortunately, decoding has many pitfalls that, if not avoided carefully, can lead to false negative or, even worse, false positive results.

Decodanda is an open-source Python package designed to expose a user-friendly and flexible interface for population activity decoding, avoiding the most common pitfalls through a series of built-in best practices.

It For example:

• Cross-validation is automatically implemented using a default or specified trial structure

• All analyses come with a built-in null model to test the significance of the data performance (see the notebook)

• Multi-session pooling to create pseudo-populations is supported out of the box (see the notebook)

• The individual contributions of multiple correlated experimental variables are isolated by cross-variable data balancing, avoiding the confounding effects of correlated variables (see the notebook)

In addition, Decodanda exposes a series of functions to compute the Cross-Condition Generalization Performance (CCGP) for the geometrical analysis of neural population activity (see the notebook).

For a guided explanation of some of the best practices implemented by Decodanda, you can refer to my teaching material for the Advanced Theory Course in Neuroscience at Columbia University.

Have fun, drink water, and decode safely!