Discriminating signal from noise: Recognition of a movement-based animal display by artificial neural networks

Date

2006

Authors

Peters, Richard
Davis, Colin J

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

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.

Description

Keywords

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

Citation

Source

Behavioural Processes

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

DOI

10.1016/j.beproc.2005.12.002

Restricted until

2037-12-31