![](/images/graphics-bg.png)
Constraints of Biological Neural Networks and Their Consideration in AI Applications
Author
Source
Advances in Artificial Intelligence
Issue
Vol. 2010, Issue 2010 (31 Dec. 2010), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-11-25
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Science
Abstract EN
Biological organisms do not evolve to perfection, but to out compete others in their ecological niche, and therefore survive and reproduce.
This paper reviews the constraints imposed on imperfect organisms, particularly on their neural systems and ability to capture and process information accurately.
By understanding biological constraints of the physical properties of neurons, simpler and more efficient artificial neural networks can be made (e.g., spiking networks will transmit less information than graded potential networks, spikes only occur in nature due to limitations of carrying electrical charges over large distances).
Furthermore, understanding the behavioural and ecological constraints on animals allows an understanding of the limitations of bio-inspired solutions, but also an understanding of why bio-inspired solutions may fail and how to correct these failures.
American Psychological Association (APA)
Stafford, Richard. 2009. Constraints of Biological Neural Networks and Their Consideration in AI Applications. Advances in Artificial Intelligence،Vol. 2010, no. 2010, pp.1-6.
https://search.emarefa.net/detail/BIM-502820
Modern Language Association (MLA)
Stafford, Richard. Constraints of Biological Neural Networks and Their Consideration in AI Applications. Advances in Artificial Intelligence No. 2010 (2010), pp.1-6.
https://search.emarefa.net/detail/BIM-502820
American Medical Association (AMA)
Stafford, Richard. Constraints of Biological Neural Networks and Their Consideration in AI Applications. Advances in Artificial Intelligence. 2009. Vol. 2010, no. 2010, pp.1-6.
https://search.emarefa.net/detail/BIM-502820
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-502820