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Self-organisation in a perceptual network (1988)
| Venue: | IEEE Computer |
| Citations: | 364 - 0 self |
BibTeX
@ARTICLE{Linsker88self-organisationin,
author = {Ralph Linsker},
title = {Self-organisation in a perceptual network},
journal = {IEEE Computer},
year = {1988},
pages = {105--117}
}
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Abstract
young animal or child perceives and identifies features in its envi-, roument in an apparently effortless way. No presently known algorithms even approach this flexible, generalpurpose perceptual capability. Discovering the principles that may underlie perceptual processing is important both for neuroscience and for the development of synthetic perceptual systems. Two important aspects of the mystery of perception are (1) What processing functions does the neural “machinery ” perform on perceptual input, and what is the circuitry that implements these functions? (2) How does this “machinery ” come to be? Unlike conventional computer hardware, neural circuitry is not hard-wired or specified as an explicit set of point-to-point connections. Instead it develops under the influence of a genetic specification and. epigenetic factors, such as electrical activity, both before and after birth. How this happens is in large part unknown. Biological development processes are far too complex to hope that a relatively complete understanding of how a perceptual system develops and functions will soon emerge. But we are familiar with complex synthetic systems, such as computers, whose principles of organization can be understood without one’s knowing How can a perceptual system develop to recognize specific features of its environment, without being told which features it should analyze, or even whether its identifications are correct? in detail how the components work. Furthermore, the same principles can be used to build computers in any of several different technologies. Might there be organizing principles (1) that explain some essential aspects of how a perceptual system develops and functions; (2) that we can attempt to infer without waiting for far more detailed experimental information; and (3) that can lead to profitable experimental programs, testable predictions, and applications to synthetic perception as well as neuroscientific understanding? I believe the answer is yes, and that the use of theoretical neural networks that embody biologically-motivated rules and constraints is a powerful tool in this study. This optimism is encouraged by recent work ’ in which I have found that a multilayered network, developing according to simple yet biologically plausible “Hebbtype” rules, * self-organizes to produce
Keyphrases
perceptual network perceptual system biological development process electrical activity identifies feature epigenetic factor large part unknown explicit set perceptual processing recent work profitable experimental program biologically-motivated rule perceptual input complete understanding neural machinery perform effortless way testable prediction specific feature plausible hebbtype rule synthetic perceptual system point-to-point connection theoretical neural network child perceives complex synthetic system multilayered network important aspect powerful tool essential aspect neural circuitry genetic specification young animal detailed experimental information synthetic perception generalpurpose perceptual capability component work conventional computer hardware several different technology
