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An Evolutionary Perspective on the Design of Neuromorphic Shape Filters
- Source :
- IEEE Access, Vol 8, Pp 114228-114238 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- A substantial amount of time and energy has been invested to develop machine vision using connectionist (neural network) principles. Most of that work has been inspired by theories advanced by neuroscientists and behaviorists for how cortical systems store stimulus information. Those theories call for information flow through connections among several neuron populations, with the initial connections being random (or at least non-functional). Then the strength or location of connections are modified through training trials to achieve an effective output, such as the ability to identify an object. Those theories ignored the fact that animals that have no cortex, e.g., fish, can demonstrate visual skills that outpace the best neural network models. Neural circuits that allow for immediate effective vision and quick learning have been preprogrammed by hundreds of millions of years of evolution and the visual skills are available shortly after hatching. Cortical systems may be providing advanced image processing, but most likely are using design principles that had been proven effective in simpler systems. The present article provides a brief overview of retinal and cortical mechanisms for registering shape information, with the hope that it might contribute to the design of shape-encoding circuits that more closely match the mechanisms of biological vision.
- Subjects :
- FOS: Computer and information sciences
0301 basic medicine
General Computer Science
Computer Science - Artificial Intelligence
Computer science
Machine vision
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Visual mechanisms
Image processing
Stimulus (physiology)
03 medical and health sciences
0302 clinical medicine
Connectionism
FOS: Electrical engineering, electronic engineering, information engineering
medicine
Biological neural network
General Materials Science
Neural and Evolutionary Computing (cs.NE)
Artificial neural network
business.industry
Image and Video Processing (eess.IV)
General Engineering
Computer Science - Neural and Evolutionary Computing
Electrical Engineering and Systems Science - Image and Video Processing
global shape filters
Artificial Intelligence (cs.AI)
030104 developmental biology
medicine.anatomical_structure
Neuromorphic engineering
FOS: Biological sciences
Quantitative Biology - Neurons and Cognition
neuromorphic circuits
Neurons and Cognition (q-bio.NC)
Neuron
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....6f7be20f750b2969887e2df2de93b32a