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Programming Molecular Systems To Emulate a Learning Spiking Neuron

Fil, Jakub, Dalchau, Neil, Chu, Dominique (2022) Programming Molecular Systems To Emulate a Learning Spiking Neuron. ACS Synthetic Biology, 11 (6). pp. 2055-2069. ISSN 2161-5063. (doi:10.1021/acssynbio.1c00625) (KAR id:96319)

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DOI for this version (Do not enter information here. For Information Services repository administration use only).: 10.1021/acssynbio.1c00625

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https://pubs.acs.org/doi/10.1021/acssynbio.1c00625

Abstract

Hebbian theory seeks to explain how the neurons in the brain adapt to stimuli to enable learning. An interesting feature of Hebbian learning is that it is an unsupervised method and, as such, does not require feedback, making it suitable in contexts where systems have to learn autonomously. This paper explores how molecular systems can be designed to show such protointelligent behaviors and proposes the first chemical reaction network (CRN) that can exhibit autonomous Hebbian learning across arbitrarily many input channels. The system emulates a spiking neuron, and we demonstrate that it can learn statistical biases of incoming inputs. The basic CRN is a minimal, thermodynamically plausible set of microreversible chemical equations that can be analyzed with respect to their energy requirements. However, to explore how such chemical systems might be engineered de novo, we also propose an extended version based on enzyme-driven compartmentalized reactions. Finally, we show how a purely DNA system, built upon the paradigm of DNA strand displacement, can realize neuronal dynamics. Our analysis provides a compelling blueprint for exploring autonomous learning in biological settings, bringing us closer to realizing real synthetic biological intelligence.

Item Type: Article
DOI/Identification number: 10.1021/acssynbio.1c00625
Uncontrolled keywords: Hebbian learning spiking neurons DNA strand displacement autonomous learning biochemical intelligence
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Dominique Chu
Date Deposited: 20 Aug 2022 10:33 UTC
Last Modified: 22 Aug 2022 09:03 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/96319 (The current URI for this page, for reference purposes)
Chu, Dominique: https://orcid.org/0000-0002-3706-2905
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