Discriminating signal from noise: Recognition of a movement-based animal display by artificial neural networks
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Peters, Richard; Davis, Colin J
Description
In this study, we investigated the feasibility of applying neural networks to understanding movement-based visual signals. Networks based on three different models were constructed, varying in their input format and network architecture: a Static Input model, a Dynamic Input model and a Feedback model. The task for all networks was to distinguish a lizard (Amphibolurus muricatus) tail-flick from background plant movement. Networks based on all models were able to distinguish the two types of...[Show more]
dc.contributor.author | Peters, Richard | |
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dc.contributor.author | Davis, Colin J | |
dc.date.accessioned | 2015-12-08T22:27:22Z | |
dc.identifier.issn | 0376-6357 | |
dc.identifier.uri | http://hdl.handle.net/1885/34047 | |
dc.description.abstract | In this study, we investigated the feasibility of applying neural networks to understanding movement-based visual signals. Networks based on three different models were constructed, varying in their input format and network architecture: a Static Input model, a Dynamic Input model and a Feedback model. The task for all networks was to distinguish a lizard (Amphibolurus muricatus) tail-flick from background plant movement. Networks based on all models were able to distinguish the two types of visual motion, and generalised successfully to unseen exemplars. We used curves defined by the receiver-operating characteristic (ROC) to select a single network from each model to be used in regression analyses of network response and several motion variables. Collectively, the models predicted that tail-flick efficacy would be enhanced by faster speeds, greater acceleration and longer durations. | |
dc.publisher | Elsevier | |
dc.source | Behavioural Processes | |
dc.subject | Keywords: artificial neural network; lizard; movement; signaling; acceleration; amphibolurus muricatus; animal experiment; article; artificial neural network; feasibility study; feedback system; lizard; model; movement (physiology); noise; nonhuman; plant; predicti Animal communication; Classification; Lizards; Movement; Neural network | |
dc.title | Discriminating signal from noise: Recognition of a movement-based animal display by artificial neural networks | |
dc.type | Journal article | |
local.description.notes | Imported from ARIES | |
local.identifier.citationvolume | 72 | |
dc.date.issued | 2006 | |
local.identifier.absfor | 060604 - Comparative Physiology | |
local.identifier.absfor | 060805 - Animal Neurobiology | |
local.identifier.ariespublication | u9204316xPUB108 | |
local.type.status | Published Version | |
local.contributor.affiliation | Peters, Richard, College of Medicine, Biology and Environment, ANU | |
local.contributor.affiliation | Davis, Colin J, Macquarie University | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.startpage | 52 | |
local.bibliographicCitation.lastpage | 64 | |
local.identifier.doi | 10.1016/j.beproc.2005.12.002 | |
dc.date.updated | 2015-12-08T09:19:13Z | |
local.identifier.scopusID | 2-s2.0-32344446472 | |
Collections | ANU Research Publications |
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