[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119109-en":3,"doc-seo-119109-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119109,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Realizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges","Artificial intelligence and machine learning are increasingly deployed across devices, motivating the search for energy-efficient computation on platforms far smaller than conventional electronics. This paper examines Molecular Machine Learning (MML), where chemical reactions must transport, process, and interpret information carried by molecules. It reviews existing MML approaches, then explores future directions using gene regulatory networks and population interactions to form neural networks. It also discusses training mechanisms in biological cells via calcium signaling and demonstrates a calcium-signaling-based analog-to-digital converter.","School of Computing: Faculty Publications  \nComputer Science and Engineering, Department of  \n6-2023  \nRealizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges  \nSasitharan Balasubramaniam  \nSamitha Somathilaka  \nSehee Sun  \nAdrian Ratwatte  \nMassimiliano Pierobon  \nFollow this and additional works at: [https://digitalcommons.unl.edu/csearticles](https://digitalcommons.unl.edu/csearticles)  \n Part of the Computer Sciences Commons  \nThis Article is brought to you for free and open access by the Computer Science and Engineering, Department of at DigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in School of Computing: Faculty Publications by an authorized administrator of DigitalCommons@University of Nebraska-Lincoln.  \nAuthor Manuscript Author Manuscript Author Manuscript Author Manuscript  \n\n|  | HHS Public Access\u003Cbr>Author manuscript\u003Cbr>IEEE Nanotechnol Mag. Author manuscript; available in PMC 2024 June 07. |\n| --- | --- |\n\nPublished in final edited form as:  \nIEEE Nanotechnol Mag. 2023 June ; 17(3): 10–20. doi:10.1109/mnano.2023.3262099 .  \nRealizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges  \nSasitharan Balasubramaniam [Senior Member, IEEE],  \nSchool of Computing, University of Nebraska-Lincoln, NE, USA  \nSamitha Somathilaka [Student, IEEE],  \nSchool of Computing, University of Nebraska-Lincoln, NE, USA Walton Institute, South East Technological University, Ireland.  \nSehee Sun,  \nSchool of Computing, University of Nebraska-Lincoln, NE, USA  \nAdrian Ratwatte [Student, IEEE],  \nSchool of Computing, University of Nebraska-Lincoln, NE, USA  \nMassimiliano Pierobon [Member, IEEE]  \nSchool of Computing, University of Nebraska-Lincoln, NE, USA  \nAbstract  \nArtificial Intelligence (AI) and Machine Learning (ML) are weaving their way into the fabric of society, where they are playing a crucial role in numerous facets of our lives. As we witness the increased deployment of AI and ML in various types of devices, we benefit from their use into energy-efficient algorithms for low powered devices. In this paper, we investigate a scale and medium that is far smaller than conventional devices as we move towards molecular systems that can be utilized to perform machine learning functions, i.e., Molecular Machine Learning (MML) . Fundamental to the operation of MML is the transport, processing, and interpretation of information propagated by molecules through chemical reactions. We begin by reviewing the current approaches that have been developed for MML, before we move towards potential new directions that rely on gene regulatory networks inside biological organisms as well as their population interactions to create neural networks. We then investigate mechanisms for training machine learning structures in biological cells based on calcium signaling and demonstrate their application to build an Analog to Digital Converter (ADC). Lastly, we look at potential future directions as well as challenges that this area could solve.  \nKeywords  \nArtificial Intelligence; Machine Learning; Molecular Communications; Synthetic Biology  \nI. INTRODUCTION  \nIn recent years we have started to witness the widespread development of systems to apply Artificial Intelligence (AI) and Machine Learning (ML) to very diverse application scenarios  \n[1] . This has resulted in software-based systems for AI, such as Artificial Neural Networks  \nAuthor Manuscript Author Manuscript Author Manuscript Author Manuscript  \nBalasubramaniam et al. Page 2  \n(ANN) [2] as well as hardware based systems like neuromorphic hardware [3] . In particular, within the area of ANN various algorithms have been developed, that includes Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), amongst others, where each has its own properties and behaviour derived from specific functions of neuronal networks ofthe brain. While develop","cbCainYr3pnobo7V","https://ap.wps.com/l/cbCainYr3pnobo7V","pdf",1715484,1,22,"English","en",105,"# Abstract\n# Keywords\n# I. Introduction","[{\"question\":\"What is Molecular Machine Learning (MML) as described in the paper?\",\"answer\":\"MML is machine learning implemented in molecular-scale systems where information is transported, processed, and interpreted through chemical reactions propagated by molecules.\"},{\"question\":\"How does the paper connect communications and biological AI to future MML directions?\",\"answer\":\"It reviews current MML approaches and then examines new directions that rely on gene regulatory networks inside organisms and population interactions to create neural-network-like functionality.\"},{\"question\":\"What biological mechanism is used to train machine learning structures in cells?\",\"answer\":\"The paper investigates training mechanisms in biological cells based on calcium signaling, and uses this to build an analog-to-digital converter (ADC).\"}]","Realizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges | 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is Molecular Machine Learning (MML) as described in the paper?","Question",{"text":75,"@type":76},"MML is machine learning implemented in molecular-scale systems where information is transported, processed, and interpreted through chemical reactions propagated by molecules.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper connect communications and biological AI to future MML directions?",{"text":80,"@type":76},"It reviews current MML approaches and then examines new directions that rely on gene regulatory networks inside organisms and population interactions to create neural-network-like functionality.",{"name":82,"@type":73,"acceptedAnswer":83},"What biological mechanism is used to train machine learning structures in cells?",{"text":84,"@type":76},"The paper investigates training mechanisms in biological cells based on calcium signaling, and uses this to build an analog-to-digital converter 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