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9 - Deep Learning

from Part II - Cognitive Modeling Paradigms

Published online by Cambridge University Press:  21 April 2023

By
Marco Gori ,
Frédéric Precioso  and
Edmondo Trentin
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

This chapter introduces deep learning (DL) in the framework of experimentalism, taking inspiration from Pierre Oleron’s explanation of human intellectual activities in terms of long (or, deep) circuits. A history of DL is presented, from its origin in the mid-twentieth century to the breakthrough of deep neural networks (DNNs) in the last decades. Architectural and representational issues are then discussed in depth. Convolutional neural networks, the most popular and successful DL algorithm to date, are reviewed in detail. Finally, adaptive activation functions in DNNs are presented in the context of homeostatic neuroplasticity, surveyed, and analyzed.

Keywords

deep learningdeep neural networkinternal representationconvolutional neural networkadaptive activation function
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 301 - 349
Publisher: Cambridge University Press
Print publication year: 2023

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