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About this paper
Renate Krause, Joanne J.A. van Bavel, Chenxi Wu, Marc A. Vos, Alain Nogaret, Giacomo Indiveri
A spiking neural network model of coupled oscillators, implemented on mixed-signal neuromorphic hardware, demonstrated robust and tunable rhythmic outputs, achieving precise control over heart chamber delays (e.g., 17 ± 0.3 ms for RA-LA) and replicating respiratory sinus arrhythmia with an R² of 0.78 against physiological data.
Key insights
- Stable and controllable rhythmic outputs can be reliably generated on intrinsically noisy and inhomogeneous neuromorphic hardware by employing population coding and activity-based activation thresholds.
- A structured tuning methodology, focusing on weight parameters and compensatory adjustments, effectively maps desired CPG properties (frequency, phase) to hardware-specific parameters.
- Neuromorphic systems are capable of enabling ultra-low power, real-time adaptive bio-inspired control, as demonstrated by the adaptive cardiac pacemaker application.
Summary by alphaXiv.
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