DOI: https://doi.org/10.1115/dmd2021-1073May 11, 2021Proceedings Paper

Neuromorphic Representation of Cardiac Data from the American Black Bear During Hibernation

Timothy G. LaskeTinen L. Iles

Abstract

Brain-inspired (neuromorphic) systems realize biological neural principles with Spiking Neural Networks (SNN) to provide high-performing, energy-efficient frameworks for robotics, artificial intelligence, and adaptive control. The Neural Engineering Framework (NEF) brings forth a theoretical framework approach for the representation of high-dimensional mathematical constructs with spiking neurons for the implementation of functional large-scale neural networks. Here, we explore the utilization of neuromorphic adaptive control for circadian modulated cardiac pacing by examining the neuromorphic representation of high-dimensional cardiac data. For this study, we have utilized a model from a data set acquired from an American black bear during hibernation. Black bears in Minnesota will hibernate for 4-6 months without eating and drinking while losing little muscle mass and remain relatively normothermic throughout the winter [10]. In the current study, we obtained EEG and ECG data from one black bear throughout the winter months in Grand Rapids, MN, represented with NEF. Our results demonstrated opposing requirements for neuromorphic representation. While using high synaptic time constants for obtained ECG data, provided desirable...Continue Reading

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