@article{scholars15696,
           pages = {855--871},
       publisher = {Springer Science and Business Media Deutschland GmbH},
         journal = {Lecture Notes in Networks and Systems},
            year = {2021},
           title = {Neuronal Unit of Thoughts (NUTs); A{\^A} Probabilistic Formalism for Higher-Order Cognition},
             doi = {10.1007/978-981-16-1089-9{$_6$}{$_6$}},
          volume = {204},
            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},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111964038&doi=10.1007\%2f978-981-16-1089-9\%5f66&partnerID=40&md5=3a0cb4771da002d950ef2819caf30f07},
        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.{\^A} 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 {\^a}??surprise,{\^a}?? is characterized, and the formalism is evaluated in the context of a non-trivial application: Autonomous Driving with imperfect sensors. {\^A}{\copyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
          author = {Zakaria, N.},
            issn = {23673370},
            isbn = {9789811610882}
}