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A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks.

Hanna Keren1,2,3, Johannes Partzsch3, Shimon Marom1,2

  • 1Department of Physiology, Biophysics and Systems Biology, Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel.

Frontiers in Neuroscience
|May 29, 2019
PubMed
Summary
This summary is machine-generated.

Researchers created a biohybrid system coupling biological neural networks with a hardware network (NeuroSoC). This novel experimental model advances neuromorphic-neural interfaces for real-time interaction with neural activity.

Keywords:
brain-machine interfacingneural couplingneural engineeringneural networksneuromorphic networks

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Area of Science:

  • Neuroscience
  • Biotechnology
  • Computer Engineering

Background:

  • Advancing neural interfaces and neuroprosthetics requires effective coupling of biological neural activity with artificial components.
  • Current technologies face challenges in real-time, bidirectional integration of biological and artificial neural networks.

Purpose of the Study:

  • To develop and present a novel biohybrid experimental setting for coupling biological and hardware neural networks.
  • To demonstrate the feasibility of real-time, bidirectional integration and control within this system.
  • To provide a model for future neuromorphic-neural interface development.

Main Methods:

  • Implementation of a biomimetic hardware network (NeuroSoC) on a VLSI chip, emulating 2880 neurons and 12.7 million synapses.
  • Coupling the NeuroSoC with an in vitro biological neural network.
  • Real-time recording, processing, and bidirectional integration of activities from both networks.
  • Development of control circuits to modulate the biohybrid system's activity.

Main Results:

  • Successful functional coupling of the biological neural network and the NeuroSoC hardware network.
  • Demonstration of adjustable and well-monitored bidirectional integration in real-time.
  • Implementation of control circuits to modify the combined biohybrid activity.
  • Characterization of the NeuroSoC's complex dynamics across multiple timescales.

Conclusions:

  • The presented biohybrid system offers a viable experimental model for advanced neuromorphic-neural interfaces.
  • This setup facilitates the study of neural network functionalities and their pathological irregularities.
  • The research paves the way for improved interfacing capabilities with neural activity.