The memristive implementation of the hippocampus: A hypothesis

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Abstract We present a hippocampus-inspired neuromorphic system based on a stochastic poly-crystalline nano-fibre mesh. This system takes advantage of the inherent randomness of the material structure to create a dynamically evolving connectivity pattern that mimics the probabilistic nature of biological synaptic networks. The memristive architecture is based on polycrystaline memristive materials derived from acetylsalycilic acid (aspirin). Three different compounds were obtained by adding axial ligands to [Cu2(asp)4]: benzimidazole (bimi), pyridine (py), and 1,4-diazabicyclo[2.2.2]octane (DABCO). The results showed that the memristive properties of devices based on copper complexes can be modulated with various axial ligands. Moreover, theoretical analysis predicts the possibility of mimicking hippocampal architecture via cocrystallization of hydrophobic organic molecular semiconductors with selected copper complexes. Additionally, we demonstrated that controlling crystallization temperature and solvent composition allows precise tuning of resistive switching in methylammonium lead iodide perovskites. These approaches together provide a versatile foundation for implementing dynamic, brain-like memory functions in hardware.
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The memristive implementation of the hippocampus: A hypothesis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The memristive implementation of the hippocampus: A hypothesis Dominik Caus, Andrzej Sławek, Tomasz Mazur, Piotr Zawal, Bogusław Baś, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6589367/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Aug, 2025 Read the published version in BioNanoScience → Version 1 posted 9 You are reading this latest preprint version Abstract We present a hippocampus-inspired neuromorphic system based on a stochastic poly-crystalline nano-fibre mesh. This system takes advantage of the inherent randomness of the material structure to create a dynamically evolving connectivity pattern that mimics the probabilistic nature of biological synaptic networks. The memristive architecture is based on polycrystaline memristive materials derived from acetylsalycilic acid (aspirin). Three different compounds were obtained by adding axial ligands to [Cu2(asp)4]: benzimidazole (bimi), pyridine (py), and 1,4-diazabicyclo[2.2.2]octane (DABCO). The results showed that the memristive properties of devices based on copper complexes can be modulated with various axial ligands. Moreover, theoretical analysis predicts the possibility of mimicking hippocampal architecture via cocrystallization of hydrophobic organic molecular semiconductors with selected copper complexes. Additionally, we demonstrated that controlling crystallization temperature and solvent composition allows precise tuning of resistive switching in methylammonium lead iodide perovskites. These approaches together provide a versatile foundation for implementing dynamic, brain-like memory functions in hardware. Full Text Additional Declarations No competing interests reported. Supplementary Files SupportingInformation2025.pdf Cite Share Download PDF Status: Published Journal Publication published 20 Aug, 2025 Read the published version in BioNanoScience → Version 1 posted Editorial decision: Revision requested 26 Jun, 2025 Reviews received at journal 16 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 12 May, 2025 Reviewers agreed at journal 10 May, 2025 Reviewers invited by journal 08 May, 2025 Editor assigned by journal 08 May, 2025 Submission checks completed at journal 08 May, 2025 First submitted to journal 04 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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