LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos

TL;DR

LeMoNADe is a novel nonparametric method that detects neuronal motifs directly from calcium imaging videos, enabling efficient analysis of neural activity patterns without spike extraction.

Contribution

It introduces a variational autoencoder-based approach with a discrete stochastic node for motif detection directly from imaging videos, bypassing traditional spike inference.

Findings

Excellent performance on simulated data with ground truth.

Uncovers candidate motifs in real brain slice videos.

Facilitates hypothesis generation for biological studies.

Abstract

Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing. We here propose LeMoNADe, a new exploratory data analysis method that facilitates hunting for motifs in calcium imaging videos, the dominant microscopic functional imaging modality in neurophysiology. Our nonparametric method extracts motifs directly from videos, bypassing the difficult intermediate step of spike extraction. Our technique augments variational autoencoders with a discrete stochastic node, and we show in detail how a differentiable reparametrization and relaxation can be used. An evaluation on simulated data, with available ground truth, reveals excellent quantitative performance. In real video data acquired from brain slices, with no ground truth available, LeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses for more focused biological investigations.