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Special Issue of Proceedings of the IEEE highlights the contributions of Electronic Engineering to Computational Neuroscience

By Mark D McDonnell

In May 2014, Proceedings of the IEEE published a Special Issue focused on elucidating the rapidly growing intersection between electronic engineering and the scientific field of computational neuroscience [1]. This field is an interdisciplinary area of scientific research in which one of the primary goals is to understand how electronic activity in brain cells and networks enables biological intelligence.

Proceedings of the IEEE: “Engineering Intelligence Systems Based on Computational Neuroscience”
The May 2014 issue of the flagship electronic engineering journal, “Proceedings of the Institute of Electrical & Electronics Engineers,” edited by Mark McDonnell, Kwabena Boahen, Auke Ijspeert and Terrence Sejnowski, http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6807530&punumber=5 is devoted to the topic “Engineering Intelligence Systems Based on Computational Neuroscience.” This special issue contains cutting-edge reviews and research articles from leading international groups that showcase some of the contributions of electronic engineers to brain science (McDonnell et. al, 2014).

The premise of the special issue, and the proposition discussed here, is that there is a rapidly growing intersection between fundamental neuroscience research and electronic engineering. This intersection is leading to advances in reverse-engineering of brain function, and new brain-inspired technologies

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The Twin Goals of Understanding and Emulating Biological Intelligence
For millennia, humans have observed animals performing physical feats that we could not match. Insects and birds fly, horses run at high speeds with great endurance, and fish survive their entire lives under water. Eventually we mastered mechanical design and energy storage to the extent that we can mimic, and in many cases surpass, the physical abilities of other animals.

Yet, although we have also discovered how to skillfully manipulate electrons in ways that have transformed society, we have failed so far at convincingly reproducing one aspect in which biological organisms, including humans, excel. Biological brains combine sensory information with past experience to make predictions and decisions that enable their survival in the physical world. That is, they exhibit intelligence.

It is therefore arguably the case that two of this century’s grandest scientific challenges are to: discover and understand the neurobiological mechanisms that support processing, learning and intelligence in biological brains; and design engineered systems that replicate the capabilities of biological intelligence.

In 2013, the importance of these challenges was recognized by the European Union, who have provided more than US$1 billion for research in the area of computational neuroscience, in the form of the “Human Brain Project.” This project’s stated aims include advancing knowledge about brain function and creating new brain-inspired computation technology. Subsequently in 2013, the US announced the similarly large-scale BRAIN initiative.

The special issue on computational neuroscience is therefore especially timely because these initiatives coincide with tremendous progress recently in the field of computational neuroscience, some of which has been specifically enabled by electrical, electronic, and computer engineers.

Contributions of electronic engineering to computational neuroscience
Computational Neuroscience is a broad interdisciplinary field, and its practitioners have diverse objectives, such as seeking understanding about the origins of mental disorders in neural circuit pathologies, designing biomedical interfaces between brains and computers, and proving mathematical theorems about neuronal information capacity.

The Special Issue therefore does not seek to review the entire field, but emphasizes three ways in which electrical, electronic and computer engineers contribute to understanding computation in the brain and building intelligent systems that utilize this knowledge: 1. Enabling technologies: the development of scientific sensors, data processing algorithms and simulation platforms used by computational neuroscientists; 2. Reverse engineering the brain: research that links empirical neuroscience evidence with hypotheses about how neurobiological systems manipulate information; 3. Biomimetic/Neuromorphic computation and robotics: imitation of neurobiological computation mechanisms in designed applications.

Advancing knowledge in life sciences by emulating brains in electronic systems
“Engineering” is said to literally mean to “make things happen.” Having such a perspective in brain-science is potentially critically important for breakthroughs in understanding how the brain’s electrical activity leads to intelligent behaviour In this newsletter, authors from three of the papers in the special issue summarize their work and its relevance to this “life sciences” question. Common to all three papers is the goal of advancing science by building electronic systems. However, the differences in their approaches also highlight the rich and broad scope for electronic engineering contributing to life sciences by “making things happen.”

The three papers in the Special Issue can be summarized as follows:

  • Franceschini (2014) [2] reviews the design and construction of small insect-like robots that navigate and control their motion using biologically inspired visual strategies and circuits designed based on knowledge from computational neuroscience.
  • Stewart and Eliasmith (2014) [3] review a system capable of performing multiple cognitive functions using a combination of biologically plausible spiking neurons, and an architecture that mimics the organization, function, and representational resources used in the mammalian brain.
  • Rahimi Azghadi et al. (2014) [4] review challenges and progress in neuromorphic implementations of timing-based neuronal learning mechanisms.

Acknowledgments
Mark D. McDonnell is supported by an Australian Research Fellowship from the Australian Research Council (project number DP1093425). The author would like to thank his co-guest-editors in the special issue of Proceedings of the IEEE on computational neuroscience: Kwabena Boahan of Stanford University, USA, Auke Ijspeert of EPFL, Switzerland, and Terrence J. Sejnowski of the Salk Institute for Biological Studies, USA. The author also gratefully acknowledges all contributions to the special issue from all authors.

For Further Reading

1. M. D. McDonnell, K. Boahen, A. Ijspeert, and T. J. Sejnowski, “Scanning the Issue: Engineering Intelligent Electronic Systems Based on Computational”. Proceedings of the IEEE, 2014. 102(5):646-651.

2. N. Franceschini, “Small Brains, Smart Machines: From Fly Vision to Robot Vision and Back Again”, Proceedings of the IEEE, 2014. 102(5):751 – 781.

3. T. C. Stewart, and C. Eliasmith, (2014). “Large-Scale Synthesis of Functional Spiking Neural Circuits”, Proceedings of the IEEE, 2014. 102(5):881-898.

4. M. Rahimi Azghadi, et al., “Spike-Based Synaptic Plasticity in Silicon: Design, Implementation, Application, and Challenges”, Proceedings of the IEEE, 2014. 102(5):717-737.

 

Contributor

Mark D. McDonnellMark D. McDonnell is a Senior Research Fellow at the Institute for Telecommunications Research, University of South Australia, where he is Principle Investigator of the Computational and Theoretical Neuroscience. He received a PhD in Electronic Engineering from The University of Adelaide, Australia His interdisciplinary research focuses on the application of computational and engineering methods to advance scientific knowledge about the influence of noise and random variability in brain signals and structures during neurobiological computation. Read more

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The IEEE Life Sciences eNewsletter is a new initiative to bring forth interesting articles and informative interviews within the exciting field of life sciences every month. Please subscribe to the eNewsletter to receive notification each month when new articles are published.

June 2014 Contributors

Mark D. McDonnellMark D. McDonnell is a Senior Research Fellow at the Institute for Telecommunications Research, University of South Australia, where he is Principle Investigator of the Computational and Theoretical Neuroscience. He received a PhD in Electronic Engineering from The University of Adelaide, Australia His interdisciplinary research focuses on the application of computational and engineering methods to advance scientific knowledge about the influence of noise and random variability in brain signals and structures during neurobiological computation. Read more

Nicolas FranceschiniNicolas Franceschini received the Doctorat d'Etat degree in physics from the University of Grenoble and National Polytechnic Institute, Grenoble, France. He was appointed as a Research Director at the C.N.R.S. and set up the Neurocybernetics Lab, and later the Biorobotics Lab in Marseille, France. His research interests include neural information processing, vision, eye movements, microoptics, neuromorphic circuits, sensory-motor control systems, biologically-inspired robots and autopilots. Read more

Mostafa RahimiMostafa Rahimi Azghadi is a PhD candidate in the University of Adelaide, Australia. His current research interests include neuromorphic learning systems, spiking neural networks and nanoelectronic. Read more

Giacomo IndiveriGiacomo Indiveri is an Associate Professor at the Faculty of Science, University of Zurich, Switzerland. His current research interests lie in the study of real and artificial neural processing systems and in the hardware implementation of neuromorphic cognitive systems, using full custom analog and digital VLSI technology. Read more

Derek AbbottDerek Abbott is a full Professor within the School of Electrical and Electronic Engineering at the University of Adelaide, Australia. His interests are in the area of multidisciplinary physics and electronic engineering applied to complex systems. Read more

Terrence C. StewartTerrence C. Stewart received a Ph.D. degree in cognitive science from Carleton University in 2007. He is a post-doctoral research associate in the Department of Systems Design Engineering with the Centre for Theoretical Neuroscience at the University of Waterloo, in Waterloo, Canada. His core interests are in understanding human cognition by building biologically realistic neural simulations, and he is currently focusing on language processing and motor control. Read more

Chris EliasmithChris Eliasmith received a Ph.D. degree in philosophy from Washington University in St. Louis in 2000. He is a full professor at the University of Waterloo. He is currently Director of the Centre for Theoretical Neuroscience at the University of Waterloo and holds a Canada Research Chair in Theoretical Neuroscience. He has authored or coauthored two books and over 100 publications in philosophy, psychology, neuroscience, computer science, and engineering venues. Read more