Systems, Man & Cybernetics - July 2015 - 17

A Novel
Architecture
Combining
the Strengths
of NTs, CNNs,
and ELMs

by Niki Martinel,
Christian Micheloni,
and Gian Luca Foresti

IMAGE LICENSED BY INGRAM PUBLISHING

The Evolution
of Neural Learning
Systems

M

imicking the human brain to achieve
human-level cognition performance
has been a core challenge in artificial intelligence research for decades.
Humans are very efficient in capturing
the most important information while being exposed to
a plethora of different stimuli, a capability that is used
to represent and understand their surroundings in a
concise fashion. Machine learning research has made
considerable progress toward cloning such a human
capability with innovative techniques like deep learning,
feature learning, incremental learning, and so on. In this
Digital Object Identifier 10.1109/MSMC.2015.2461151
Date of publication: 4 March 2016

2333-942X/15©2015IEEE

article, an overview of the mainstream brain-inspired
architectures and research directions proposed over the
past decade is provided. In addition, a novel architecture
exploiting the strengths of the current methods is proposed. Preliminary results demonstrate that it is able to
achieve state-of-the-art results in a more efficient way.
Neural Learning:
Mimicking the Human Brain
We, as humans, are inherently able to complete a plethora
of different and complex tasks in fractions of a second. In
particular, in every instant of our chaotic and busy lives, we
are exposed to myriad data acquired through many different sensors, but we are somehow able to retain the relevant
aspects of such data in a way that allows for their future
Ju ly 2015

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE

17



Table of Contents for the Digital Edition of Systems, Man & Cybernetics - July 2015

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