{"title":"Brain theory and cooperative computation.","authors":"M A Arbib","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>\"Top-down\" brain theory, based upon functional analysis of cognitive processes in terms of interacting schemas, is distinguished from \"bottom-up\" brain theory based on analysis of the dynamics of neural nets. \"Cooperative computation\" is proposed as the style of interaction of neural subsystems at various levels. Perceptual schemas are introduced as the building blocks for the representation of the perceived environment, and motor schemas serve as control systems to be coordinated into programs for the control of movement. A cooperative computation view of the design of machine vision systems is exemplified both by an algorithm for computing optic flow which offers interesting insights into the evolution of hierarchical neural structures, and by an analysis of knowledge representation for machine interpretation of visual scenes. The interaction between top-down analysis and detailed neural modelling is illustrated by the study of visuomotor coordination in frogs and toads.</p>","PeriodicalId":77724,"journal":{"name":"Human neurobiology","volume":"4 4","pages":"201-18"},"PeriodicalIF":0.0000,"publicationDate":"1985-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human neurobiology","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
"Top-down" brain theory, based upon functional analysis of cognitive processes in terms of interacting schemas, is distinguished from "bottom-up" brain theory based on analysis of the dynamics of neural nets. "Cooperative computation" is proposed as the style of interaction of neural subsystems at various levels. Perceptual schemas are introduced as the building blocks for the representation of the perceived environment, and motor schemas serve as control systems to be coordinated into programs for the control of movement. A cooperative computation view of the design of machine vision systems is exemplified both by an algorithm for computing optic flow which offers interesting insights into the evolution of hierarchical neural structures, and by an analysis of knowledge representation for machine interpretation of visual scenes. The interaction between top-down analysis and detailed neural modelling is illustrated by the study of visuomotor coordination in frogs and toads.