SpikeCanvas: An IoT-based control plane for event-triggered workflow orchestration of high-density neurophysiology data

Electrophysiology instruments generate continuous, high-volume data streams during experiments. Processing of these data efficiently requires coordination across distributed computing resources. Longitudinal recordings from high-density microelectrode arrays (HD-MEAs) are particularly challenging to manage due to their scale, heterogeneous software dependencies, and limited computational capacity at data acquisition sites. To address these challenges, we present an event-triggered analysis workflow that utilizes an Internet of Things (IoT) messaging protocol as a lightweight control plane to coordinate data transfer, storage, and computation. By treating IoT messaging as a control-plane abstraction, this approach separates workflow coordination from the software systems that execute analysis tasks, enabling coordination without the need for centralized workflow schedulers or heavyweight runtime environments. We integrated cloud-based storage and elastic computing resources to support automated processing while reducing dependence on local software installations. Analysis services and algorithms are containerized to allow workflows to be composed and executed under constrained infrastructure conditions. We examine this approach through case studies including in vitro neural activity recordings from cortical organoids and ex vivo brain slices, demonstrating that IoT-style workflow orchestration can support multiscale electrophysiology analysis in real experimental settings.