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
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
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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
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