  Multi-electrode arrays (MEAs) can record extracellular action potentials
(also known as 'spikes') from hundreds or thousands of neurons simultaneously.
Inference of a functional network from a spike train is a fundamental and
formidable computational task in neuroscience. With the advancement of MEA
technology, it has become increasingly crucial to develop statistical tools for
analyzing multiple neuronal activity as a network. In this paper, we propose a
scalable Bayesian framework for inference of functional networks from MEA data.
Our framework makes use of the hierarchical structure of networks of neurons.
We split the large scale recordings into smaller local networks for network
inference, which not only eases the computational burden from Bayesian sampling
but also provides useful insights on regional connections in organoids and
brains. We speed up the expensive Bayesian sampling process by using parallel
computing. Experiments on both synthetic datasets and large-scale real-world
MEA recordings show the effectiveness and efficiency of the scalable Bayesian
framework. Inference of networks from controlled experiments exposing neural
cultures to cadmium presents distinguishable results and further confirms the
utility of our framework.
