Properties of neuronal facilitation that improve target tracking in natural pursuit simulations.
(2015) In Journal of the Royal Society Interface 12(108).- Abstract
- Although flying insects have limited visual acuity (approx. 1°) and relatively small brains, many species pursue tiny targets against cluttered backgrounds with high success. Our previous computational model, inspired by electrophysiological recordings from insect 'small target motion detector' (STMD) neurons, did not account for several key properties described from the biological system. These include the recent observations of response 'facilitation' (a slow build-up of response to targets that move on long, continuous trajectories) and 'selective attention', a competitive mechanism that selects one target from alternatives. Here, we present an elaborated STMD-inspired model, implemented in a closed loop target-tracking system that uses... (More)
- Although flying insects have limited visual acuity (approx. 1°) and relatively small brains, many species pursue tiny targets against cluttered backgrounds with high success. Our previous computational model, inspired by electrophysiological recordings from insect 'small target motion detector' (STMD) neurons, did not account for several key properties described from the biological system. These include the recent observations of response 'facilitation' (a slow build-up of response to targets that move on long, continuous trajectories) and 'selective attention', a competitive mechanism that selects one target from alternatives. Here, we present an elaborated STMD-inspired model, implemented in a closed loop target-tracking system that uses an active saccadic gaze fixation strategy inspired by insect pursuit. We test this system against heavily cluttered natural scenes. Inclusion of facilitation not only substantially improves success for even short-duration pursuits, but it also enhances the ability to 'attend' to one target in the presence of distracters. Our model predicts optimal facilitation parameters that are static in space and dynamic in time, changing with respect to the amount of background clutter and the intended purpose of the pursuit. Our results provide insights into insect neurophysiology and show the potential of this algorithm for implementation in artificial visual systems and robotic applications. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7486893
- author
- Bagheri, Zahra M ; Wiederman, Steven D ; Cazzolato, Benjamin S ; Grainger, Steven and O'Carroll, David LU
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of the Royal Society Interface
- volume
- 12
- issue
- 108
- article number
- 20150083
- publisher
- The Royal Society of Canada
- external identifiers
-
- pmid:26063815
- wos:000358824600020
- scopus:84936775265
- pmid:26063815
- ISSN
- 1742-5662
- DOI
- 10.1098/rsif.2015.0083
- language
- English
- LU publication?
- yes
- id
- 051fcbb3-c614-4ead-b55d-d79b64cff36a (old id 7486893)
- date added to LUP
- 2016-04-01 10:43:19
- date last changed
- 2022-04-12 08:59:04
@article{051fcbb3-c614-4ead-b55d-d79b64cff36a,
abstract = {{Although flying insects have limited visual acuity (approx. 1°) and relatively small brains, many species pursue tiny targets against cluttered backgrounds with high success. Our previous computational model, inspired by electrophysiological recordings from insect 'small target motion detector' (STMD) neurons, did not account for several key properties described from the biological system. These include the recent observations of response 'facilitation' (a slow build-up of response to targets that move on long, continuous trajectories) and 'selective attention', a competitive mechanism that selects one target from alternatives. Here, we present an elaborated STMD-inspired model, implemented in a closed loop target-tracking system that uses an active saccadic gaze fixation strategy inspired by insect pursuit. We test this system against heavily cluttered natural scenes. Inclusion of facilitation not only substantially improves success for even short-duration pursuits, but it also enhances the ability to 'attend' to one target in the presence of distracters. Our model predicts optimal facilitation parameters that are static in space and dynamic in time, changing with respect to the amount of background clutter and the intended purpose of the pursuit. Our results provide insights into insect neurophysiology and show the potential of this algorithm for implementation in artificial visual systems and robotic applications.}},
author = {{Bagheri, Zahra M and Wiederman, Steven D and Cazzolato, Benjamin S and Grainger, Steven and O'Carroll, David}},
issn = {{1742-5662}},
language = {{eng}},
number = {{108}},
publisher = {{The Royal Society of Canada}},
series = {{Journal of the Royal Society Interface}},
title = {{Properties of neuronal facilitation that improve target tracking in natural pursuit simulations.}},
url = {{http://dx.doi.org/10.1098/rsif.2015.0083}},
doi = {{10.1098/rsif.2015.0083}},
volume = {{12}},
year = {{2015}},
}