Orders > Conference proceedings > 12th international workshop on systems, signals and image processing
(from Chapter 1: Invited Addresses and Tutorials on Signals, Coding,
 Systems and Intelligent Techniques)
| Full Citation and Abstract
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Title: |
Neural Network Principles for Organisms in Nonstationary Environments |
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Author(s): |
D. S. Levine |
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Address: |
University of Texas at Arlington, USA
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Reference: |
SSIP-SP1, 2005 pp. 5 - 5 |
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Abstract/
Summary |
Responses required of biological organisms in nonstationary environments pose complementary challenges for engineering design. For example, vision and other sensory systems must obtain sufficiently accurate representations of external events, yet modify these representations in order to perceive regularities in the environment. Motor control systems must perform the same movement at variable speeds and with variable effectors, yet interrupt a movement when other environmental contingencies need to be addressed. Categorization systems must learn novel events, yet not forget much older events. These systems also must self-organize categories based on regularities in the external world, yet dynamically weight input attributes in response to changes in task requirements.
After sixty years, neural network models inspired by these design issues are approaching the structure and functional organization of the brain. This has led neural network theories such as adaptive resonance to be useful in engineering applications ranging from radar signal detection to robotics to medical and financial data classification. At the same time, these network theories have predicted and been partially confirmed by recent results on the organization of brain areas such as visual and motor areas of the cortex. |
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