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The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
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IMPROBED: Multiple Problem-Solving Brain via Evolved Developmental Programs.

Julian Francis Miller1

  • 1University of York, Department of Computer Science. julian.miller@york.ac.uk.

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|November 8, 2021
PubMed
Summary

This study introduces IMPROBED, a novel artificial brain model using evolutionary and developmental programs to construct neural networks. The model successfully builds adaptable networks capable of solving multiple computational problems simultaneously.

Keywords:
Cartesian genetic programmingComputational neuro-inspired developmentevolutionary algorithmsgeneral artificial intelligence

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Area of Science:

  • Computational neuroscience
  • Developmental robotics
  • Artificial intelligence

Background:

  • Artificial neural networks (ANNs) lack biological realism, as they typically do not incorporate evolutionary and developmental processes fundamental to brain construction.
  • Existing ANNs are often designed for single tasks, limiting their adaptability and efficiency.

Purpose of the Study:

  • To introduce IMPROBED, a novel neural model that utilizes evolutionary and developmental programs to construct an artificial brain.
  • To demonstrate that this model can simultaneously solve multiple computational problems using a single, evolving network.

Main Methods:

  • Development of two core neural programs: one controlling neuron soma behavior (movement, change, death, replication) and another controlling dendrite behavior (extension, change, death, replication).
  • Employing these programs to evolve a single artificial neural network that dynamically changes over time.
  • Defining new problem classes suitable for evaluating developmental approaches to neural network construction.

Main Results:

  • The evolved programs successfully constructed a single, adaptable neural network.
  • Multiple conventional ANNs, each specialized for a different computational problem, were successfully extracted from the single evolved network.
  • The IMPROBED model demonstrated the capability to solve multiple computational problems concurrently.

Conclusions:

  • The IMPROBED model offers a biologically plausible approach to artificial neural network design by integrating evolution and development.
  • This method allows for the creation of versatile artificial brains capable of handling multiple tasks simultaneously.
  • The general nature of the approach suggests broad applicability across various computational problems and AI research domains.