Papers › Engineering Calcium Signaling of Astrocytes for Neural-Molecular Computing Logic Gates
Engineering Calcium Signaling of Astrocytes for Neural-Molecular Computing Logic Gates
Michael Taynnan Barros, Phuong Doan, Meenakshisundaram Kandhavelu, Brendan Jennings, Sasitharan Balasubramaniam
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
This paper proposes the use of Eukaryotic cells, namely astrocytes, to develop logic gates. The logic gates are achieved by manipulating the threshold of Ca²⁺ ion flows between the cells, based on the input signals. Through wet-lab experiments that engineer the astrocytes cells with pcDNA3.1-hGPR17 genes, we show that both AND and OR gates can be implemented by controlling Ca²⁺ signals that flow through the population. A reinforced learning platform is also presented in the paper to optimize two main parameters, which are the Ca²⁺ activation threshold and time slot of input signals T_b into the gate. This design platform caters for any size and connectivity of the cell population, by taking into consideration the delay and noise produced from the signalling between the cells, in order to fine-tune the activation threshold and input signal time slot parameters. To validate the effectiveness of the reinforced learning platform, a Ca²⁺ Signalling-based Molecular Communications Simulator was used to simulate the signalling between the astrocyte cells. The results from the simulation showed that an optimum value for both the Ca²⁺ activation threshold and time slot of input signals T_b is required to achieve optimal computation accuracy, where up to 90\% accuracy for both the AND and OR gates can be achieved with the right combination of values. The reinforced learning platform for the engineered astrocytes to create digital logic gates can be used for future Neural-Molecular Computing chip, which can revolutionize brain implants that are constructed from engineered biological cells.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections