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
Neural Networks
Neural networks, which are also called artificial neural networks, are an information processing paradigm that imitates biological nervous systems, such as the brain. The biological neural network inside the human brain is composed of a group or groups of chemically connected or functionally associated neurons, which could perform a specific physiological function, such as pattern recognition and decision making. Similarly, the artificial neural network system is composed of many simple processing elements operating in parallel. The function of the processing elements is for example determined by network structure and connection strengths.
First attempt of neural network research goes back to 1943 when the first artificial neuron was produced by the neurophysiologist McCulloch and the logician Walter Pits. However the computing technology was limited at that era, and they couldn’t do anything further; but their studies laid foundations for later work in neural network research. [1]
So far, the neural network has been a real movement of the discipline in three different directions:
Artificial neural networks (ANNs)
Neuromorphic Systems
Computational Neuroscience
First attempt of neural network research goes back to 1943 when the first artificial neuron was produced by the neurophysiologist McCulloch and the logician Walter Pits. However the computing technology was limited at that era, and they couldn’t do anything further; but their studies laid foundations for later work in neural network research. [1]
So far, the neural network has been a real movement of the discipline in three different directions:
Artificial neural networks (ANNs)
Neuromorphic Systems
Computational Neuroscience
Subscribe to:
Post Comments (Atom)
Conclusion
Neural networks possess the ability to learn from examples, which makes them very powerful, and have extended the world of computing and improved a lot for real life application as well. Neural networks also contribute to areas of neuroscience and neurophysiology, which shed the light on mimicking or synthesizing human brains. Future application of neural networks will have more potential when it is integrated with other AI computing approaches, such as fuzzy logics and robotics, and provide the better environment for human life.
Bibliography
1. Müller, Berndt, Joachim Reinhardt, and Michael Thomas Strickland. Neural Networks: An Introduction. 2nd ed.
2. Lisboa, Paulo J., Emmanuel C. Ifeachor, and Piotr S. Szczepaniak. Artificial Neural Networks in Biomedicine.
3. Krogh, Anders. “What are Artificial Neural Networks?” Nature Biotechnology 26 (2008): 195-197.
4. Carsten, Stephan, Henning Cammann, and Klaus Jung. “Artificial Neural Nnetworks: Has the Time Come for Their Use in Prostate Cancer Patients?” Nature Clinical Practice. Urology 2 (2005): 262-263.
5. Lisboa, Paulo J., and Azzam F.G. Taktak. “The Use of Artificial Neural Networks in Decision Support in Cancer: A Systematic Review” Neural Networks 19 (2006): 408–415.
6. Silver, Rae, Kwabena Boahen, Sten Grillner, Nancy Kopell, and Kathie L. Olsen. “Neurotech for Nneuroscience: Unifying Concepts, Organizing Principles, and Emerging Tools” Journal of Neuroscience 27 (2007):11807-11819.
7. Boahen, Kwabena. “Brains in Silicon.” Department of Bioengineering at
8. Lynch, Zack. “Blue Brain Project Moves onto Whole Brain, Really?” Corante.com 28 Nov. 2007. 26 Apr. 2008.
9. MArkram, Henry. “The Blue Brain Project.” Nature Reviews Neuroscience 7 (2006): 153-160.
10. Blue Brain Project. 2008. IBM and EPFL 20 Apr. 2008
11. Bressan, Beatrice. “The Greatest Challenge: Computing the Brain.” Interview. Cerncourier.com 20 Aug. 2007
No comments:
Post a Comment