UC Santa Cruz

Extracellular neuronal recordings offer a high-resolution and real-time measurement of individual neuron activity and network dynamics. The rapid evolution of neural interface technologies, including microfabricated probes and multielectrode arrays, provides access to various neuronal experiment paradigms. Recent advancements in recording platforms exponentially increase neuronal data's volume and complexity, which requires efficient storage, processing, and interpretation.

To address these challenges, we developed a lightweight, low-cost neuronal recording system using the Internet of Things (IoT) to achieve remote recordings with high-quality signal acquisition, enabling scalability and reducing lab labor. We also established an IoT cloud biology laboratory that serves as a comprehensive cloud-based infrastructure for multimodal data management. This infrastructure integrates various data streams, including electrophysiology, microscopy imaging, and microfluidics. This cloud laboratory provides permanent data storage and data viewing, processing, and sharing functions.

Furthermore, we created a multiscale electrophysiology data pipeline that utilizes IoT messaging protocols with cloud computing technologies for large-scale analysis of longitudinal neuronal recordings. We containerized the analysis algorithms as the pipeline's building blocks for scalability and flexibility. We designed graphical user interfaces and command line tools to erase the requirement of programming skills. The interactive visualizations provide multi-modality information on various neuronal features. This cloud-based pipeline is an efficient solution for electrophysiology data processing, the limitations of local software tools, and storage constraints. This application simplifies the electrophysiology data analysis process and facilitates the understanding of in-vitro neuronal activity.

We have applied our work to study the epileptiform activity in human hippocampus slices. With optogenetics stimulation, we successfully ceased the seizure-like event by suppressing the activity of excitatory neurons. We further analyzed the neuron types by their waveform and associated them with the location in the hippocampus. Neurons as different levels of responders to the light have shown differences in the firing rate change.

In conclusion, these systems enable automated data collection, rapid processing, and analysis of complex neural dynamics for large-scale neuronal recordings. The integration of IoT technologies with cloud computing resources provides solutions for remote experiment control, data sharing, and reproducible analysis workflows. Our work significantly contributes to in vitro electrophysiology methods and computational approaches for handling high-dimensional neuroscience data. These tools improve our understanding of fundamental neural processes and have broad applications in translational studies of neurological disorders. By providing deeper insights into neural circuit function, development, and pathology, this research paves the way for future investigations to unravel the complexities of brain function and its implications for health and disease.