Synthetic neuromorphic computing in living cells
Name
s41467-022-33288-8.pdf
Description
Published version
Size
4.99 MB
Format
Adobe PDF
Checksum (MD5)
311537f81f0b68c75fa2f3f348c506d4
Author(s)
Rizik, Luna
Danial, Loai
Habib, Mouna
Weiss, Ron
Daniel, Ramez
Date Issued
September 24, 2022
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Rizik, Luna, Danial, Loai, Habib, Mouna, Weiss, Ron and Daniel, Ramez. 2022. "Synthetic neuromorphic computing in living cells." Nature Communications, 13 (1).
Version
Final published version
Abstract
AbstractComputational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Analogously to their revolutionary impact on computing, neuro-inspired models can transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and readily reconfigurable for different tasks. To this end, we introduce the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in Escherichia coli cells. We successfully modify perceptgene parameters to create devices that encode a minimum, maximum, and average of analog inputs. With these devices, we create multi-layer perceptgene circuits that compute a soft majority function, perform an analog-to-digital conversion, and implement a ternary switch. We also create a programmable perceptgene circuit whose computation can be modified from OR to AND logic using small molecule induction. Finally, we show that our approach enables circuit optimization via artificial intelligence algorithms.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41467-022-33288-8
