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Related Experiment Video

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Neuron-Type-Specific Utility in a Brain-Machine Interface: a Pilot Study.

Martha G Garcia-Garcia1,2, Austin J Bergquist2, Hector Vargas-Perez3

  • 1a Institute of Biomaterials and Biomedical Engineering, University of Toronto , Canada.

The Journal of Spinal Cord Medicine
|September 14, 2017
PubMed
Summary
This summary is machine-generated.

Fast-spiking and bursting neurons show high utility in brain-machine interfaces (BMIs), unlike regular-spiking neurons. Intrinsic properties can predict neuron usefulness for improved BMI training and performance.

Keywords:
Brain-computer interfaceBrain-machine interfaceFiring rateModulationMotor cortexNeuronOperant conditioningSpike widthUpregulationWaveform width

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Area of Science:

  • Neuroscience
  • Bioengineering

Background:

  • Voluntarily controlling single cortical neuron firing rates via biofeedback is possible for brain-machine interfaces (BMIs).
  • Identifying neurons suitable for operant conditioning is crucial for advancing BMI clinical applications.

Purpose of the Study:

  • To determine criteria for predicting neuron utility in BMI implementation.
  • To investigate the relationship between intrinsic electrophysiological properties and neuron responsiveness to operant conditioning.

Main Methods:

  • Extracellular recordings from 7 single cortical neurons in the primary motor cortex of Long-Evans rats.
  • A BMI system was used to control light-emitting-diode brightness by up-regulating neuronal firing rates.
  • Neurons were classified as fast-spiking, bursting, or regular-spiking based on waveform and firing patterns.

Main Results:

  • Fast-spiking and bursting neurons increased firing rates significantly (2.43±1.16 fold), indicating high utility.
  • Regular-spiking neurons decreased firing rates on average (0.73±0.23 fold), demonstrating low utility.
  • A clear contrast in utility was observed between neuron types.

Conclusions:

  • Intrinsic electrophysiological properties, such as neuron type, can predict utility in BMI applications.
  • Selecting neurons based on these properties may minimize training times and maximize information yield in clinical BMIs.