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Reconstructing creative thoughts: Hopfield neural networks.

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Title: Reconstructing creative thoughts: Hopfield neural networks.
Authors: Checiu, Denisa1 (AUTHOR), Bode, Mathias1 (AUTHOR), Khalil, Radwa1,2 (AUTHOR) rkhalil@constructor.university
Source: Neurocomputing. Mar2024, Vol. 575, pN.PAG-N.PAG. 1p.
Subjects: Hopfield networks, Creative thinking, Artificial intelligence, Semantic memory, Problem solving
Abstract: From a brain processing perspective, the perception of creative thinking is rooted in the underlying cognitive process, which facilitates exploring and cultivating novel avenues and problem-solving strategies. However, it is challenging to emulate the intricate complexity of how the human brain presents a novel way to uncover unique solutions. One potential approach to mitigating this complexity is incorporating creative cognition into the evolving artificial intelligence systems and associated neural models. Hopfield neural network (HNN) are commonly acknowledged as a simplified neural model, renowned for their biological plausibility to store and retrieve information, specifically patterns of neurons. Our findings suggest utilizing modern HNN to emulate creative thinking by making meaningful associations between seemingly disparate concepts. This semantic link is represented as a radio knob that can be set to determine whether the network solves problems creatively or shuts down; the threshold is a parameter. We used the term "first knob of creativity" to describe a certain pattern and utilized the "second knob of creativity" to aid in the examination of alternatives within the network. By manipulating the knobs, it is possible to selectively suppress specific patterns, facilitating the creative functioning of the HNN and identifying other patterns with which input can be linked. [ABSTRACT FROM AUTHOR]
Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: From a brain processing perspective, the perception of creative thinking is rooted in the underlying cognitive process, which facilitates exploring and cultivating novel avenues and problem-solving strategies. However, it is challenging to emulate the intricate complexity of how the human brain presents a novel way to uncover unique solutions. One potential approach to mitigating this complexity is incorporating creative cognition into the evolving artificial intelligence systems and associated neural models. Hopfield neural network (HNN) are commonly acknowledged as a simplified neural model, renowned for their biological plausibility to store and retrieve information, specifically patterns of neurons. Our findings suggest utilizing modern HNN to emulate creative thinking by making meaningful associations between seemingly disparate concepts. This semantic link is represented as a radio knob that can be set to determine whether the network solves problems creatively or shuts down; the threshold is a parameter. We used the term "first knob of creativity" to describe a certain pattern and utilized the "second knob of creativity" to aid in the examination of alternatives within the network. By manipulating the knobs, it is possible to selectively suppress specific patterns, facilitating the creative functioning of the HNN and identifying other patterns with which input can be linked. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.neucom.2024.127324
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
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      – SubjectFull: Hopfield networks
        Type: general
      – SubjectFull: Creative thinking
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Semantic memory
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      – SubjectFull: Problem solving
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            NameFull: Bode, Mathias
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              M: 03
              Text: Mar2024
              Type: published
              Y: 2024
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