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dc.contributor.authorSalge, C.
dc.contributor.authorPolani, D.
dc.identifier.citationSalge , C & Polani , D 2009 , ' Information-driven organization of visual receptive fields ' , Advances in Complex Systems , vol. 12 , no. 3 , pp. 311-326 .
dc.identifier.otherPURE: 87717
dc.identifier.otherPURE UUID: ff03b3f4-60d7-47be-a6f5-6433f6249e1a
dc.identifier.otherdspace: 2299/6034
dc.identifier.otherScopus: 68349141400
dc.descriptionPublished as Advances in Complex Systems 12 (3) pp.311-326 DOI: 10.1142/S0219525909002234 copyright World Scientific Publishing Company at: [Full text of this article is not available in the UHRA]
dc.description.abstractBy using information theory to reduce the state space of sensor arrays, such as receptive fields, for AI decision making we offer an adaptive algorithm without classical biases of hand coded approaches. This paper presents a way to build an acyclic directed graph to organize the sensor inputs of a visual receptive field. The Information Distance Metric is used to repeatedly select two sensors, which contain the most information about each other. Those are then encoded to a single variable, of equal alphabet size, with a deterministic mapping function that aims to create maximal entropy while maintaining a low information distance to the original sensors. The resulting tree determines which sensors are fused to reduce the input data while maintaining a maximum of information. The structure adapts to different environments of input images by encoding groups of preferred line structures or creating a higher resolution for areas with simulated movement. These effects are created without prior assumptions about the sensor statistics or the spatial configuration of the receptive field, and are cheap to compute since only pair-wise informational comparison of sensors is used.en
dc.relation.ispartofAdvances in Complex Systems
dc.subjectinformation theory
dc.subjectadaptive sensors
dc.titleInformation-driven organization of visual receptive fieldsen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionAdaptive Systems
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Engineering and Computer Science
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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