Cognitive Mapping and Episodic Memory Emerge From Simple Associative Learning Rules

Cognitive Mapping and Episodic Memory Emerge From Simple Associative Learning Rules | 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 Article Cognitive Mapping and Episodic Memory Emerge From Simple Associative Learning Rules Ekaterina Gribkova, Girish Chowdhary, Rhanor Gillette This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2733393/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 May, 2024 Read the published version in Neurocomputing → Version 2 posted You are reading this latest preprint version Show more versions Abstract Episodic memory enables animals to map contexts and environmental features in space and time but is underused in artificial intelligence (AI). Here we show how simple associative learning rules can be expanded to basic episodic memory in AI. We augment an agent-based foraging simulation, ASIMOV, modeled on the simple neuronal circuitry of an invertebrate forager, by adding a novel computational module for simple episodic memory, the Feature Association Matrix (FAM). The FAM is a set of computationally light, graph learning algorithms which functionally resemble the auto- and hetero-associative circuits of the hippocampus for episodic memory. In simulations, FAM enables highly efficient foraging and navigation and shows how higher-order conditioning mechanisms give rise to spatial cognitive mapping by chaining pair-wise associations and encoding them with additional contexts. Thus, FAM demonstrates a biologically inspired, bottom-up enhancement of AI for higher-order cognition. Biological sciences/Neuroscience/Computational neuroscience/Learning algorithms Biological sciences/Neuroscience/Cognitive neuroscience/Intelligence Episodic memory cognitive mapping spatial learning computational model agent-based simulation Full Text Additional Declarations The authors declare no competing interests. Supplementary Files ASIMOVFAMSupplementaryInfoNComm.pdf Supplementary Information FAMSupplementaryVideoS1.mp4 Supplementary Video Cite Share Download PDF Status: Published Journal Publication published 01 May, 2024 Read the published version in Neurocomputing → Version 2 posted You are reading this latest preprint version Show more versions 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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