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Minds and Machines
Published in: Minds and Machines 4/2018

05-10-2018

Computational Functionalism for the Deep Learning Era

Author: Ezequiel López-Rubio

Published in: Minds and Machines | Issue 4/2018

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Abstract

Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of deep learning, namely the extraction of successively higher level features and the multiple layer structure, which are closely related to each other. Then some indications about the framing of this heated debate are given. After that, an assessment of the value of artificial deep networks as models of the human brain is given from the similarity perspective of model representation. Finally, a new version of computational functionalism is proposed which addresses the specificity of deep neural computation better than classic, program based computational functionalism.
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Metadata
Title
Computational Functionalism for the Deep Learning Era
Author
Ezequiel López-Rubio
Publication date
05-10-2018
Publisher
Springer Netherlands
Published in
Minds and Machines / Issue 4/2018
Print ISSN: 0924-6495
Electronic ISSN: 1572-8641
DOI
https://doi.org/10.1007/s11023-018-9480-7

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