A pipeline for interpretable neural latent discovery

Mechanistic understanding of the brain requires interpretability of large-scale neuronal computations. Many latent variable model approaches applied to such datasets excel at decoding but yield opaque latent spaces. We address this with NLDisco, a pipeline for interpretable neural latent discovery. Motivated by successful applications of sparse dictionary learning in AI mechanistic interpretability, NLDisco encourages hidden layer neurons in sparse encoder-decoder models to learn interpretable structure in dense neural activity. A flexible, open-source software package supports interpretable latent discovery across recording modalities and experimental paradigms. We validate the pipeline on both synthetic and real extracellular recordings across species and brain regions, showing that it recovers ground-truth features in synthetic data and reveals meaningful representations in real data. These results highlight the pipeline's potential to advance neuroscientific discovery and provide a foundation for future applications and extensions.