Synthetic biological neural networks : from current implementations to future perspectives

Halužan Vasle, Ana (author), Moškon, Miha (author)

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning.

• Language: English
• Keywords: neural networks, perceptron, synthetic biology, molecular computing, neuromorphic computing, modelling and simulation
• Work type: Article
• Typology: 1.02 - Review Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: 11 str.
• Numbering: Vol. 237, art. 105164
• PID: 20.500.12556/RUL-155774
• UDC: 004:575.112
• ISSN on article: 0303-2647
• DOI: 10.1016/j.biosystems.2024.105164
• COBISS.SI-ID: 187310083
• Publication date in RUL: 17.04.2024

Record is a part of a journal

• Title: Biosystems
• Shortened title: Biosystems
• Publisher: North-Holland
• ISSN: 0303-2647
• COBISS.SI-ID: 25103360

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: nevronske mreže, sintezna biologija, molekularno računalništvo, nevromorfno računalništvo, modeliranje in simulacije

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0359
• Name: Vseprisotno računalništvo

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: J1-50024
• Name: Povezava med hipoksijo in sintezo holesterola v cirkadianem času