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 . 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 low pass filtering, representation of EEG data requires fast synapses and a high number of neurons. Although this is only an analysis of a small sample of the data available, these guidelines provided the robust pilot dataset to observe the SNN patterns during prolonged hibernation and pair this data with the cardiac responses and thus support research questions related to the autonomic tone during hibernation. This preliminary research will help further develop our neuromorphic adaptive controller to better adapt cardiac pacing to circadian rhythms. This unique dataset may pave the way toward deciphering the underlying neural mechanisms of hibernation, providing translational to humans.
|Original language||English (US)|
|Title of host publication||Proceedings of the 2021 Design of Medical Devices Conference, DMD 2021|
|Publisher||American Society of Mechanical Engineers|
|State||Published - 2021|
|Event||2021 Design of Medical Devices Conference, DMD 2021 - Virtual, Online|
Duration: Apr 12 2021 → Apr 15 2021
|Name||Proceedings of the 2021 Design of Medical Devices Conference, DMD 2021|
|Conference||2021 Design of Medical Devices Conference, DMD 2021|
|Period||4/12/21 → 4/15/21|
Bibliographical noteFunding Information:
We would like to acknowledge Dr. David Garshelis and the Minnesota DNR team, including Spencer Rettler and Dr. Andrew Tri, for their expertise and fieldwork with the bear population in Minnesota, Dr. Lars Mattison, Myana Anderson, the Visible Heart Laboratories staff, and Tamara Pearlman Tsur for the insightful comments. Medtronic LLC for the use of the LINQ™ devices and stations. This research was supported by the Open University of Israel Research Grant.
© 2021 by ASME.