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Neuronal Unit of Thoughts (NUTs); A Probabilistic Formalism for Higher-Order Cognition

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Communication and Intelligent Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 204))

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 ‘surprise,’ is characterized, and the formalism is evaluated in the context of a non-trivial application: Autonomous Driving with imperfect sensors.

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Author information

Authors and Affiliations

  1. High-Performance Cloud Computing Center, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

    Nordin Zakaria

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  1. Nordin Zakaria
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Correspondence to Nordin Zakaria .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Jaipur, India

    Mukesh Kumar Gupta

  3. Birla Institute of Applied Sciences, Nainital, Uttarakhand, India

    G. S. Tomar

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Wang Lipo

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Zakaria, N. (2021). Neuronal Unit of Thoughts (NUTs); A Probabilistic Formalism for Higher-Order Cognition. In: Sharma, H., Gupta, M.K., Tomar, G.S., Lipo, W. (eds) Communication and Intelligent Systems. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1089-9_66

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  • DOI: https://doi.org/10.1007/978-981-16-1089-9_66

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1088-2

  • Online ISBN: 978-981-16-1089-9

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