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A programmable neural virtual machine based on a fast store-erase learning rule.

Garrett E Katz1, Gregory P Davis2, Rodolphe J Gentili3

  • 1Department of Elec. Engr. and Comp. Sci., Syracuse University, Syracuse, NY, USA.

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|August 4, 2019
PubMed
Summary
This summary is machine-generated.

We developed a Neural Virtual Machine (NVM) that executes symbolic programs using neurocomputation. This novel architecture enables fast, local learning for versatile program execution, mimicking traditional computer functionality.

Keywords:
Itinerant attractor dynamicsLocal learningMultiplicative gatingProgrammable neural networksSymbolic processing

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Area of Science:

  • Neurocomputation
  • Computer Architecture
  • Artificial Intelligence

Background:

  • Traditional computer architectures rely on complex hardware for symbolic program execution.
  • Existing programmable neural networks often lack efficiency or flexibility in program representation and execution.

Purpose of the Study:

  • To introduce a novel neural architecture, the Neural Virtual Machine (NVM), capable of representing and executing arbitrary symbolic programs.
  • To demonstrate that a purely neurocomputational approach can replicate the functionality of traditional computer architectures.

Main Methods:

  • Development of a novel local learning rule for neurocomputational program representation.
  • Implementation of principles such as fast non-iterative learning, distributed representations, and itinerant attractor dynamics.
  • Theoretical analysis and empirical computer experiments to evaluate NVM performance.

Main Results:

  • The Neural Virtual Machine (NVM) effectively represents and executes symbolic programs using a neurocomputational approach.
  • The NVM architecture supports key functionalities of traditional computer architectures.
  • Empirical experiments validated the performance and effectiveness of the NVM.

Conclusions:

  • The NVM offers a new paradigm for programmable neural networks, merging neurocomputation with symbolic program execution.
  • This architecture demonstrates the potential of purely neurocomputational systems to perform complex computational tasks.
  • The NVM's unique learning rules and dynamics enable efficient and flexible program handling.