relation: https://khub.utp.edu.my/scholars/15696/ title: Neuronal Unit of Thoughts (NUTs); A Probabilistic Formalism for Higher-Order Cognition creator: Zakaria, N. description: 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. publisher: Springer Science and Business Media Deutschland GmbH date: 2021 type: Article type: PeerReviewed identifier: 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 relation: 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 relation: 10.1007/978-981-16-1089-9₆₆ identifier: 10.1007/978-981-16-1089-9₆₆