%X 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 �surprise,� 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.
%D 2021
%R 10.1007/978-981-16-1089-9₆₆
%O 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
%J Lecture Notes in Networks and Systems
%L scholars15696
%T Neuronal Unit of Thoughts (NUTs); AÂ Probabilistic Formalism for Higher-Order Cognition
%I Springer Science and Business Media Deutschland GmbH
%V 204
%A N. Zakaria
%P 855-871