Towards Generalizable Machine Learning in Neuroscience using Graph Neural Networks

Machine learning and neuroscience have enjoyed a golden era of prosperity over the past decade as the perfect confluence of technological advances have enabled extraordinary experiments and discovery. Though tightly intertwined in the past, advances in both fields have largely diverged such that the application of deep learning techniques to microscopic neural systems remains relatively unexplored. In this thesis, I present work bridging recent advances in machine learning and neuroscience. Specifically, relying on recent advances in whole-brain imaging, we examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. We show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, we compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen C. elegans worms. These results imply a potential path to generalizable machine learning in neuroscience where pre-trained models are evaluated on unseen individuals