Neuronal Architectures for Pattern-theoretic Problems (1994)
| Venue: | Large-Scale Theories of the Cortex |
| Citations: | 65 - 1 self |
BibTeX
@INPROCEEDINGS{Mumford94neuronalarchitectures,
author = {David Mumford},
title = {Neuronal Architectures for Pattern-theoretic Problems},
booktitle = {Large-Scale Theories of the Cortex},
year = {1994},
pages = {125--152},
publisher = {MIT Press}
}
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Abstract
this paper is the proposition that the computational analysis of vision -- and speech, tactile sensing, motor control, etc. -- (the theory of the computation as Marr called it (Marr, 82)) has is reaching a point where it can provide a clearer and deeper description of the essential tasks of vision as well as a wide range of other cognitive tasks. For instance, the development of algorithms for character recognition or for face recognition or for road tracking from a moving vehicle (three problems which have been much studied on account of their potential applications) forces the researcher to deal with noisy, complex real world data. In doing this, one's initial ideas about what parts of the problem are difficult, what parts are simple, may turn out to be quite wrong. Quite often, a step which one thinks of as a simple pre-processing clean up operation turns out to be very difficult and pinpoints for you a new class of problems which had been ignored. Introspection turns out often to be very poor guide to the complexity of a problem. The reason for this, we believe, is our subjective impression of perceiving instantaneously and effortlessly the significance of sensory patterns, e.g. the word being spoken or which face is being seen. Many psychological experiments however have shown that what we perceive is not the true sensory signal, but a rational reconstruction of what the signal should be. This means that the messy ambiguous raw signal never makes it to our consciousness but gets overlaid with a clearly and precisely patterned version which could never have been computed without the extensive use of memories, expectations and logic. Only when you attempt to duplicate such a skill by computer do you discover all the hidden complexity in the computation. We believe ...
