Title

A Unified Approach To Evolving Plasticity And Neural Geometry

Abstract

An ambitious long-term goal for neuroevolution, which studies how artificial evolutionary processes can be driven to produce brain-like structures, is to evolve neurocontrollers with a high density of neurons and connections that can adapt and learn from past experience. Yet while neuroevolution has produced successful results in a variety of domains, the scale of natural brains remains far beyond reach. This paper unifies a set of advanced neuroevolution techniques into a new method called adaptive evolvable-substrate HyperNEAT, which is a step toward more biologically- plausible artificial neural networks (ANNs). The combined approach is able to fully determine the geometry, density, and plasticity of an evolving neuromodulated ANN. These complementary capabilities are demonstrated in a maze-learning task based on similar experiments with animals. The most interesting aspect of this investigation is that the emergent neural structures are beginning to acquire more natural properties, which means that neuroevolution can begin to pose new problems and answer deeper questions about how brains evolved that are ultimately relevant to the field of AI as a whole. © 2012 IEEE.

Publication Date

8-22-2012

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/IJCNN.2012.6252826

Socpus ID

84865092906 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84865092906

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