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A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.

Julie Dethier1, Paul Nuyujukian2, Chris Eliasmith3

  • 1Department of Bioengineering, Stanford University, CA 94305.

Advances in Neural Information Processing Systems
|October 14, 2014
PubMed
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This summary is machine-generated.

Researchers developed a low-power spiking neural network (SNN) for brain-machine interfaces (BMI) to restore motor function. This system shows promise for clinical translation of neural motor prostheses by reducing power consumption.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Motor prostheses aim to restore lost function in disabled patients, but clinical translation is hindered by the need for low-power, fully-implantable systems.
  • Minimizing power dissipation is crucial to prevent tissue damage from implanted devices.

Purpose of the Study:

  • To implement and test a low-power, spiking neural network (SNN) based decoder for brain-machine interface (BMI) applications.
  • To assess the feasibility of using SNNs for real-time decoding of motor intent in closed-loop systems.

Main Methods:

  • A Kalman filter-based decoder was trained to predict arm velocity in a rhesus monkey.
  • The Kalman filter was mapped onto a 2,000-neuron spiking neural network (SNN) using the Neural Engineering Framework (NEF).

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  • The SNN implementation was tested in real-time for closed-loop BMI experiments.

    • Main Results:

    • The SNN-based decoder achieved real-time performance comparable to the standard Kalman filter.
    • The closed-loop performance of the SNN decoder demonstrated its efficacy in predicting arm velocity.

    Conclusions:

    • The successful implementation of a real-time SNN decoder offers a promising pathway for low-power neural motor prostheses.
    • Hardware implementations of SNNs on neuromorphic chips could provide the necessary power savings for clinical translation of BMI technology.