Artificial neural networks (ANNs)

The power and usefulness of artificial neural networks have been demonstrated successfully in several real world applications including speech synthesis, diagnostic problems, biomedicine, business and finance, robotic control, signal processing, computer vision and many other problems that fall under the category of pattern recognitions [2,3].

Example: Artificial Neural Networks(ANNs) in medicine and cancer research

Artificial Neural Networks are currently a popular research field in medicine. At the moment, the research is mostly focused on modeling and recognizing disease or cancers inside the human body. For example, many complex factors are involved in the prediction of several diseases or cancers. Therefore, ANNs could be utilized to evaluate complex nonlinear relations among many single variables and have been demonstrated to be promising tools for improving diagnosis, staging, and prognosis of prostate cancer with the accuracy of 79–84%.[4]

Interview with Prof. Azzam F.G. Taktak in Department of Clinical Engineering at Royal Liverpool University Hospital, UK. His research areas focus on computation and biomedicine, and he has served as the reviewer for various scientific journals, such as Artificial Intelligence in Medicine, International Journal of Artificial Intelligence Tools, Neural Networks, and European Journal of Neurology. He had published several papers and one of those is a review article to discuss the advantages of using ANNs as decision making tools in the field of cancer. [5][Dr. Azzam F.G. Taktak’s professional link]

In the interview I conducted with Prof. Taktak to discuss the development and future of ANNs in biomedicine, he first clarified that many people have some misconception about the ANNs because they think ANNs work like “black magic”. He said that in real case ANNs technologies have solid theoretic foundation and he also emphasized that ANNs and other Machine learning algorithms are very useful tools in analyzing problems that are too complex for the human brain to solve. They have a major role to play in high-dimensional datasets (such as micro array data) where traditional statistical models fail. Nevertheless, the only drawback is that a very large number of samples are required to make sure that the answers they provide is a sensible one. “And such datasets are not yet widely available” he said.

Since the limitation lies with the quality of the data rather than the model, Prof. Taktak is hoping to see more emphasis on real-world applications on the use of AI/ANNs rather than focusing on developing the methodology alone. And he anticipated that ANNs could be of great value to medical application when the accumulated clinical dataset is available in the future.

Other useful websites or links:
Artificial Neural Networks in Medicine World Map
Field distribution of ANNs
Real world application of ANNs
Experts in ANNs
Neural betwork centers worldwide

Neuromorphic Systems

Other than ANNs which are mainly software computation for solving static problems on computers, the area of neuromorphic systems is concerned with implementations of sensory and neural systems on silicon and mimics the architecture and design of biological neurons. This area offers possibilities to emulate human senses and pattern recognition capabilities and then create so called intelligence.

Example: Synthetic brain project conducted by Dr. Kwabena Boahen

Dr. Boahen is a professor in Department of Bioengineering at Stanford University who received the NIH Director’s Pioneer Award (2006). His main research goal is to design computer chips to mimic how human’s brain works and learn cognitive abilities. They adopted MEMS approaches, such as CMOS and VLSI (very large scale integrated circuit), to mimic neurons with transistors in the electronic medium. They have successfully built a simulation platform called Neurogrid. It is a multi-chip system that consists of 56-by-256 array of silicon neurons in each neurocores, and able to emulate a million neurons in the cortex with computing capability comparable to 200 racks of world famous Blue Gene supercomputers. [6,7]

Other useful websites or links:
Neuromorphic Engineering Links
Center for Neuromorphic Systems Engineering, California Institute of Technology
Institute of Neuroinformatics (INI), University of Zurich, Switzerland
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Computational Neuroscience

Computational Neuroscience is an interdisciplinary science aiming to combine the ANNs and neurophysiology and attempting to unravel biological neural networks of the brains. Also, Computational Neuroscience focuses on studying the relationship between functional neurons and their physiology. Therefore, it could provide a better understanding of network behaviors and consciousness development.

Example: Blue brain Project

Lead by IBM and EPFL (Ecole Polytechnique Fédérale de Lausannethe), the project attempts to study the brain and explores how the brain works, which could possibly build accurate models of the brain. In November 2007, they announced the completion of Phase I of the Blue Brain Project to demonstrate a proof-of-principle simulation. Their results of model simulation achieve the biological fidelity with the consistency of neurobiological data, and they claims that the next step is to completely model the entire mammal brain within three years, and a human brain within the next decade. And ultimately the established models will be able to allow neuroscientists to study the brains. [8-10]
Published interview with Dr. Beatrice Bressan [11]

Other useful websites or links:
Other available software to simulate realistic environments for neuron systems constructing:
GENESIS (The GEneral NEural SImulation System)
NEURON
Computational Neuroscience on the World Wide Web
Research groups worldwide