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Robust neuromorphic coupled oscillators for adaptive pacemakers.

Renate Krause1, Joanne J A van Bavel2, Chenxi Wu3

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This study presents a robust spiking neural network model for neural coupled oscillators, demonstrating reliable control on neuromorphic hardware. This system enables ultra-low power adaptive cardiac pacemakers, showcasing robustness in variable electronic environments.

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Area of Science:

  • Neuroscience
  • Electronic Engineering
  • Biomedical Engineering

Background:

  • Neural coupled oscillators are fundamental in theoretical and simulation studies of spiking neural networks.
  • Neuromorphic electronic circuits offer new platforms for implementing these oscillators on low-power hardware.
  • Challenges exist in implementing these systems on noisy, low-precision, and inhomogeneous substrates.

Purpose of the Study:

  • To present a robust spiking neural network model of neural coupled oscillators.
  • To validate the model's implementation on a mixed-signal neuromorphic processor.
  • To demonstrate reliable control and modulation of oscillator frequency and phase shift.

Main Methods:

  • Development of a spiking neural network model for neural coupled oscillators.
  • Implementation and validation on a mixed-signal neuromorphic processor.
  • Testing robustness against variability in silicon synapse and neuron properties.

Main Results:

  • Demonstrated reliable control and modulation of oscillator frequency and phase shift.
  • Successfully built an adaptive cardiac pacemaker using the ultra-low power neural processing system.
  • Showcased the system's ability to modulate heart rate based on respiration phases.

Conclusions:

  • The implemented model is robust on highly variable neuromorphic hardware.
  • The system provides a versatile toolbox for applications requiring rhythmic outputs, such as pacemakers.
  • This work extends the capabilities of neuromorphic hardware for biological control applications.