A survey of brain inspired technologies for engineering

Cognitive engineering is a multi-disciplinary field and hence it is difficult to find a review article consolidating the leading developments in the field. The incredible pace at which technology is advancing pushes the boundaries of what is achievable in cognitive engineering. There are also differing approaches to cognitive engineering brought about from the multi-disciplinary nature of the field and the vastness of possible applications. Thus research communities require more frequent reviews to keep up to date with the latest trends. In this paper we shall discuss some of the approaches to cognitive engineering holistically to clarify the reasoning behind the different approaches and to highlight their strengths and weaknesses. We shall then show how developments from seemingly disjointed views could be integrated to achieve the same goal of creating cognitive machines. By reviewing the major contributions in the different fields and showing the potential for a combined approach, this work intends to assist the research community in devising more unified methods and techniques for developing cognitive machines.

[1]  R. Sun Artificial Intelligence: Connectionist and Symbolic Approaches , 1999 .

[2]  Geoffrey E. Hinton Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1991 .

[3]  Paul S. Rosenbloom,et al.  The Sigma cognitive architecture and system , 2013 .

[4]  Dharmendra S. Modha,et al.  A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm , 2011, 2011 IEEE Custom Integrated Circuits Conference (CICC).

[5]  P. Zelazo,et al.  What is Cognitive Control , 2013 .

[6]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[7]  Paul Smolensky,et al.  Connectionist AI, symbolic AI, and the brain , 1987, Artificial Intelligence Review.

[8]  A. Thomas,et al.  Memristor-based neural networks , 2013 .

[9]  Farnood Merrikh-Bayat,et al.  Training and operation of an integrated neuromorphic network based on metal-oxide memristors , 2014, Nature.

[10]  Jim D. Garside,et al.  Overview of the SpiNNaker System Architecture , 2013, IEEE Transactions on Computers.

[11]  Lyle N. Long,et al.  A cognitive robotic system based on the soar cognitive architecture for mobile robot navigation, search, and mapping missions , 2011 .

[12]  John E. Laird,et al.  Enhancing intelligent agents with episodic memory , 2012, Cognitive Systems Research.

[13]  Steve B. Furber,et al.  The SpiNNaker Project , 2014, Proceedings of the IEEE.

[14]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[15]  J. Laird,et al.  Enhancing intelligent agents with episodic memory Action editor : Vasant Honavar , 2011 .

[16]  John E. Laird,et al.  Extending the Soar Cognitive Architecture , 2008, AGI.

[17]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[18]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

[19]  Benjamin Naumann The Architecture Of Cognition , 2016 .

[20]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[21]  Peter I. Corke,et al.  Visual Place Recognition: A Survey , 2016, IEEE Transactions on Robotics.

[22]  Rodrigo Alvarez-Icaza,et al.  Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations , 2014, Proceedings of the IEEE.

[23]  Zachary Chase Lipton A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[24]  SchmidhuberJürgen Deep learning in neural networks , 2015 .

[25]  Lyle N. Long,et al.  A hybrid symbolic and sub-symbolic intelligent system for mobile robots , 2009 .

[26]  Pat Langley,et al.  Cognitive architectures: Research issues and challenges , 2009, Cognitive Systems Research.

[27]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[28]  Enrico Macii,et al.  The Human Brain Project and neuromorphic computing. , 2013, Functional neurology.