eprintid: 15696 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/56/96 datestamp: 2023-11-10 03:30:19 lastmod: 2023-11-10 03:30:19 status_changed: 2023-11-10 02:00:08 type: article metadata_visibility: show creators_name: Zakaria, N. title: Neuronal Unit of Thoughts (NUTs); A Probabilistic Formalism for Higher-Order Cognition ispublished: pub note: cited By 1; Conference of 2nd International Conference on Communication and Intelligent Systems, ICCIS 2020 ; Conference Date: 26 December 2020 Through 27 December 2020; Conference Code:261899 abstract: A probabilistic graphical model, Neuronal Unit of Thoughts (NUTs), is proposed in this paper that offers a formalism for the integration of lower-level cognitions. Nodes or neurons in NUTs represent sensory data or mental concepts or actions, and edges the causal relation between them. A node affects a change in the Action Potential (AP) of its child node, triggering a value change once the AP reaches a fuzzy threshold. Multiple NUTs may be crossed together producing a novel NUTs. The transition time in a NUTs, in response to a â��surprise,â�� is characterized, and the formalism is evaluated in the context of a non-trivial application: Autonomous Driving with imperfect sensors. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. date: 2021 publisher: Springer Science and Business Media Deutschland GmbH official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111964038&doi=10.1007%2f978-981-16-1089-9_66&partnerID=40&md5=3a0cb4771da002d950ef2819caf30f07 id_number: 10.1007/978-981-16-1089-9₆₆ full_text_status: none publication: Lecture Notes in Networks and Systems volume: 204 pagerange: 855-871 refereed: TRUE isbn: 9789811610882 issn: 23673370 citation: Zakaria, N. (2021) Neuronal Unit of Thoughts (NUTs); A Probabilistic Formalism for Higher-Order Cognition. Lecture Notes in Networks and Systems, 204. pp. 855-871. ISSN 23673370