Matches in UGent Biblio for { <https://biblio.ugent.be/publication/726906#aggregation> ?p ?o. }
Showing items 1 to 35 of
35
with 100 items per page.
- aggregation classification "P1".
- aggregation creator person.
- aggregation creator person.
- aggregation date "2009".
- aggregation format "application/pdf".
- aggregation hasFormat 726906.bibtex.
- aggregation hasFormat 726906.csv.
- aggregation hasFormat 726906.dc.
- aggregation hasFormat 726906.didl.
- aggregation hasFormat 726906.doc.
- aggregation hasFormat 726906.json.
- aggregation hasFormat 726906.mets.
- aggregation hasFormat 726906.mods.
- aggregation hasFormat 726906.rdf.
- aggregation hasFormat 726906.ris.
- aggregation hasFormat 726906.txt.
- aggregation hasFormat 726906.xls.
- aggregation hasFormat 726906.yaml.
- aggregation isPartOf urn:isbn:978-3-642-04273-7.
- aggregation isPartOf urn:issn:0302-9743.
- aggregation isPartOf urn:issn:1611-3349.
- aggregation language "eng".
- aggregation publisher "Springer".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "Unsupervised learning in reservoir computing: modeling hippocampal place cells for small mobile robots".
- aggregation abstract "Biological systems (e.g., rats) have efficient and robust localization abilities provided by the so called, place cells, which are found in the hippocampus of rodents and primates (these cells encode locations of the animal's environment). This work seeks to model these place cells by employing three (biologically plausible) techniques: Reservoir Computing (RC), Slow Feature Analysis (SFA), and Independent Component Analysis (ICA). The proposed architecture is composed of three layers, where the bottom layer is a dynamic reservoir of recurrent nodes with fixed weights. The upper layers (SFA and ICA) provides a self-organized formation of place cells, learned in a unsupervised way. Experiments show that a simulated mobile robot with 17 noisy short-range distance sensors is able to self-localize in its environment with the proposed architecture, forming a spatial representation which is dependent on the robot direction.".
- aggregation authorList BK120659.
- aggregation endPage "756".
- aggregation startPage "747".
- aggregation volume "5768".
- aggregation aggregates 1138025.
- aggregation isDescribedBy 726906.
- aggregation similarTo 978-3-642-04274-4_77.
- aggregation similarTo LU-726906.