pocketz.doi.bio/2026-03-20_183521_pubmed_36153343

Synthetic neuromorphic computing in living cells - PubMed

URL: https://pubmed.ncbi.nlm.nih.gov/36153343/

Saved: 2026-03-20T01:35:21.689Z

Nat Commun . 2022 Sep 24;13(1):5602. doi: 10.1038/s41467-022-33288-8. Synthetic neuromorphic computing in living cells Luna Rizik # 1, Loai Danial # 2, Mouna Habib # 1, Ron Weiss 3 4, Ramez Daniel 5 Affiliations expand PMID: 36153343 PMCID: PMC9509348 DOI: 10.1038/s41467-022-33288-8 Abstract

Computational 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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1. Perceptgene theory and implementation.

a …

Fig. 2. Perceptgenes based on auto-negative and…

Fig. 3. Multilayer perceptgene network and backpropagation…

Fig. 4. Genetically encoded data converters based…

Fig. 5. A programmable two-input perceptgene network.

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