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Artificial Development of Biologically Plausible Neural-Symbolic Networks

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Abstract

Neural-symbolic networks are neural networks designed for the purpose of representing logic programs. One of the motivations behind this is to work towards a biologically plausible model of knowledge representation in the brain. This paper reviews work in this direction and suggests that a new direction to take would be to evolve neural-symbolic networks using artificial development, which also has some biological plausibility. This idea is supported by a review of artificial development, followed by some initial results in using artificial development to evolve a neural-symbolic SHRUTI network in order to demonstrate how the fields of neural-symbolic integration and artificial development may be integrated. The experiments were successful in evolving genomes which could develop connections between neurons in working SHRUTI networks.

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Notes

  1. One exception is Learning Classifier Systems [64], which are beyond the scope of this review.

  2. Though not cited in any papers, Murray Shanahan coined this term in a presentation at COGRIC, the slides for which can be found at: http://www.cogric.reading.ac.uk/presentations/murray_shanahan.ppt.

  3. SHRUTI is a Sanskrit word meaning “what is heard directly”. The name was chosen because the SHRUTI developers draw a parallel between the dynamic pattern of acoustic energy required to encode “what is heard” and the dynamic patterns of neuron spikes which SHRUTI uses to propagate information.

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  1. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road, Exeter, EX4 4QF, UK

    Joe Townsend, Ed Keedwell & Antony Galton

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Correspondence to Joe Townsend.

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Townsend, J., Keedwell, E. & Galton, A. Artificial Development of Biologically Plausible Neural-Symbolic Networks. Cogn Comput 6, 18–34 (2014). https://doi.org/10.1007/s12559-013-9217-0

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