On Computational Models of Theory of Mind and the Imitative Reinforcement Learning in Spiking Neural Networks

On Computational Models of Theory of Mind and the Imitative Reinforcement Learning in Spiking Neural Networks | 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 On Computational Models of Theory of Mind and the Imitative Reinforcement Learning in Spiking Neural Networks Mohammad Ganjtabesh, Ashena G. Mohammadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3341817/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2024 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Theory of Mind is referred to the ability of inferring other's mental states, and it plays a crucial role in social cognition and learning. Biological evidences indicate that complex circuits are involved in this ability, including the mirror neuron system. The mirror neuron system influences imitation abilities and action understanding, leading to learn through observing others. To simulate this imitative learning behavior, a Theory-of-Mind-based Imitative Reinforcement Learning (ToM-based ImRL) framework is proposed. Employing the bio-inspired spiking neural networks and the mechanisms of the mirror neuron system, ToM-based ImRL is a bio-inspired computational model which enables an agent to effectively learn how to act in an interactive environment through observing an expert, inferring its goals, and imitating its behaviors. The aim of this paper is to review some computational attempts in modeling ToM and to explain the proposed ToM-based ImRL framework which is tested in the environment of River Raid game from Atari 2600 series. Biological sciences/Neuroscience/Computational neuroscience/Learning algorithms Biological sciences/Neuroscience/Computational neuroscience Biological sciences/Neuroscience/Computational neuroscience/Network models Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Nov, 2023 Reviews received at journal 31 Oct, 2023 Reviewers agreed at journal 30 Oct, 2023 Reviews received at journal 06 Oct, 2023 Reviews received at journal 21 Sep, 2023 Reviewers agreed at journal 12 Sep, 2023 Reviewers agreed at journal 12 Sep, 2023 Reviewers invited by journal 12 Sep, 2023 Editor assigned by journal 12 Sep, 2023 Editor invited by journal 12 Sep, 2023 Submission checks completed at journal 12 Sep, 2023 First submitted to journal 10 Sep, 2023 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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