Projecting the gene knockdown-induced phenotypes onto this axis pointed toward the role of dysregulated excitatory synaptic activity in driving the aberrant activity phenotype. 7/

We then showed that we can map relevant signal processing features onto the learned features to enable physiological interpretability, using the knockdown of the gene KCNQ2 as an example. 5/

We then developed a CRISPRi-enabled iPSC-derived astrocyte and neuron co-culture model to study the effect of 52 genetic perturbations on neuronal activity dynamics. Using the learned embeddings, we recovered ~1.5x as many generalizable phenotypes compared to manual feature engineering. 4/

We validate that these learned embeddings outperform signal processing-based manual features and traditional masked autoencoders when used for the linear classification of distinct simulated and experimental neuronal activity phenotypes. 3/

In this project, we developed a self-supervised model we call Plexus, which uses a novel single-cell encoding strategy to efficiently learn patterns of both intrinsic excitibility and network-level neuronal activity measured via calcium imaging. 2/