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Light-weight electrophysiology hardware and software platform for cloud-based neural recording experiments.

Kateryna Voitiuk1, Jinghui Geng2, Matthew G Keefe3

  • 1Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95060, United States of America.

Journal of Neural Engineering
|October 19, 2021
PubMed
Summary

Researchers developed Piphys, an affordable, open-source platform for neural recording. This system uses a Raspberry Pi and web interface for accessible, scalable electrophysiology experiments, overcoming current limitations.

Keywords:
IoTdata acquisitionelectrophysiologyin vitroneural recordingopen sourcescalable

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Area of Science:

  • Neuroscience
  • Biotechnology
  • Engineering

Background:

  • Monitoring neural activity is crucial for understanding brain function.
  • Current electrophysiology methods are limited by high costs and manual supervision, hindering large-scale studies.
  • A need exists for accessible, scalable, and automated neural recording solutions.

Purpose of the Study:

  • To develop an inexpensive, open-source neurophysiological recording platform called Piphys.
  • To enable remote control and horizontal scalability for long-term neural recording experiments.
  • To overcome the throughput limitations of current electrophysiology techniques.

Main Methods:

  • Utilized a Raspberry Pi as the central processing unit.
  • Integrated an Intan bioamplifier with a custom-designed hardware expansion circuit board.
  • Developed software for voltage sampling, user interaction, and control via a web interface using Internet of Things (IoT) protocols.

Main Results:

  • Successfully validated the Piphys system with primary human neurons.
  • Demonstrated reliable collection of neural activity data in near real-time.
  • Established a standalone system capable of remote operation and scalable data acquisition.

Conclusions:

  • Piphys offers an affordable and accessible solution for neurophysiological recordings.
  • The platform's design facilitates remote control and scalability for long-term, large-scale neural activity monitoring.
  • This system has the potential to advance research in neural development, organization, and function.