Cardiac Muscle Cell‐Based Coupled Oscillator Network for Collective Computing

• Main Authors: Xiang Ren, Jorge Gomez, Mohammad Khairul Bashar, Jiaying Ji, Uryan Isik Can, Hsueh-Chia Chang, Nikhil Shukla, Suman Datta, Pinar Zorlutuna
• Format: Article
• Language: English
• Published: Wiley 2021-04-01
• Series: Advanced Intelligent Systems
• Subjects: biocomputing; bio-oscillator; cardiomyocytes; collective computing
• Online Access: https://doi.org/10.1002/aisy.202000253

Current rate of data generation and the need for real‐time data analytics can benefit from new computational approaches where computation proceeds in a massively parallel way while being scalable and energy efficient. Biological systems arising from interaction of living cells can provide such pathways for sustainable computing. Current designs for biocomputing leveraging the information processing units of the cells, such as DNA, gene, or protein circuitries, are inherently slow (hours to days speed) and, therefore, are primarily being considered for archival storage of information. On the contrary, electrically active cells that can synchronize in milliseconds and can be connected as networks to perform massively parallel tasks can transform biocomputing and lead to novel ways of high throughput information processing. Herein, coupled oscillator networks made of living cardiac muscle cells, or bio‐oscillators, is explored as collective computing components for solving computationally hard problems. An empirically validated circuit compatible macromodel is developed for the bio‐oscillators and the fibroblast cells acting as coupling elements, to faithfully reproduce the synchronization dynamics of the network and it is shown that such bio‐oscillator network can be scaled up to hundreds of nodes and be used to solve computationally hard problems faster than traditional heuristics‐based Boolean algorithms.