Neuronal Unit of Thoughts (NUTs); AÂ Probabilistic Formalism for Higher-Order Cognition
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 affec...
| Main Author: | Zakaria, N. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.29472 / |
| Published: |
Springer Science and Business Media Deutschland GmbH
2021
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| Online Access: |
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 http://eprints.utp.edu.my/29472/ |
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utp-eprints.294722022-03-25T02:07:26Z Neuronal Unit of Thoughts (NUTs); A Probabilistic Formalism for Higher-Order Cognition Zakaria, N. 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. Springer Science and Business Media Deutschland GmbH 2021 Article NonPeerReviewed 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 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. http://eprints.utp.edu.my/29472/ |
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| 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 �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. |
| format |
Article |
| author |
Zakaria, N. |
| spellingShingle |
Zakaria, N. Neuronal Unit of Thoughts (NUTs); AÂ Probabilistic Formalism for Higher-Order Cognition |
| author_sort |
Zakaria, N. |
| title |
Neuronal Unit of Thoughts (NUTs); AÂ Probabilistic Formalism for Higher-Order Cognition |
| title_short |
Neuronal Unit of Thoughts (NUTs); AÂ Probabilistic Formalism for Higher-Order Cognition |
| title_full |
Neuronal Unit of Thoughts (NUTs); AÂ Probabilistic Formalism for Higher-Order Cognition |
| title_fullStr |
Neuronal Unit of Thoughts (NUTs); AÂ Probabilistic Formalism for Higher-Order Cognition |
| title_full_unstemmed |
Neuronal Unit of Thoughts (NUTs); AÂ Probabilistic Formalism for Higher-Order Cognition |
| title_sort |
neuronal unit of thoughts (nuts); aâ probabilistic formalism for higher-order cognition |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2021 |
| 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 http://eprints.utp.edu.my/29472/ |
| _version_ |
1741197247125651456 |
| score |
11.62408 |