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Method and software for using m-sequences to characterize parallel components of higher-order visual tracking behavior in Drosophila
A moving visual figure may contain first-order signals defined by variation in mean luminance, as well as second-order signals defined by constant mean luminance and variation in luminance envelope, or higher-order signals that cannot be estimated by taking higher moments of the luminance distributi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4215624/ https://www.ncbi.nlm.nih.gov/pubmed/25400550 http://dx.doi.org/10.3389/fncir.2014.00130 |
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author | Aptekar, Jacob W. Keles, Mehmet F. Mongeau, Jean-Michel Lu, Patrick M. Frye, Mark A. Shoemaker, Patrick A. |
author_facet | Aptekar, Jacob W. Keles, Mehmet F. Mongeau, Jean-Michel Lu, Patrick M. Frye, Mark A. Shoemaker, Patrick A. |
author_sort | Aptekar, Jacob W. |
collection | PubMed |
description | A moving visual figure may contain first-order signals defined by variation in mean luminance, as well as second-order signals defined by constant mean luminance and variation in luminance envelope, or higher-order signals that cannot be estimated by taking higher moments of the luminance distribution. Separating these properties of a moving figure to experimentally probe the visual subsystems that encode them is technically challenging and has resulted in debated mechanisms of visual object detection by flies. Our prior work took a white noise systems identification approach using a commercially available electronic display system to characterize the spatial variation in the temporal dynamics of two distinct subsystems for first- and higher-order components of visual figure tracking. The method relied on the use of single pixel displacements of two visual stimuli according to two binary maximum length shift register sequences (m-sequences) and cross-correlation of each m-sequence with time-varying flight steering measurements. The resultant spatio-temporal action fields represent temporal impulse responses parameterized by the azimuthal location of the visual figure, one STAF for first-order and another for higher-order components of compound stimuli. Here we review m-sequence and reverse correlation procedures, then describe our application in detail, provide Matlab code, validate the STAFs, and demonstrate the utility and robustness of STAFs by predicting the results of other published experimental procedures. This method has demonstrated how two relatively modest innovations on classical white noise analysis—the inclusion of space as a way to organize response kernels and the use of linear decoupling to measure the response to two channels of visual information simultaneously—could substantially improve our basic understanding of visual processing in the fly. |
format | Online Article Text |
id | pubmed-4215624 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-42156242014-11-14 Method and software for using m-sequences to characterize parallel components of higher-order visual tracking behavior in Drosophila Aptekar, Jacob W. Keles, Mehmet F. Mongeau, Jean-Michel Lu, Patrick M. Frye, Mark A. Shoemaker, Patrick A. Front Neural Circuits Neuroscience A moving visual figure may contain first-order signals defined by variation in mean luminance, as well as second-order signals defined by constant mean luminance and variation in luminance envelope, or higher-order signals that cannot be estimated by taking higher moments of the luminance distribution. Separating these properties of a moving figure to experimentally probe the visual subsystems that encode them is technically challenging and has resulted in debated mechanisms of visual object detection by flies. Our prior work took a white noise systems identification approach using a commercially available electronic display system to characterize the spatial variation in the temporal dynamics of two distinct subsystems for first- and higher-order components of visual figure tracking. The method relied on the use of single pixel displacements of two visual stimuli according to two binary maximum length shift register sequences (m-sequences) and cross-correlation of each m-sequence with time-varying flight steering measurements. The resultant spatio-temporal action fields represent temporal impulse responses parameterized by the azimuthal location of the visual figure, one STAF for first-order and another for higher-order components of compound stimuli. Here we review m-sequence and reverse correlation procedures, then describe our application in detail, provide Matlab code, validate the STAFs, and demonstrate the utility and robustness of STAFs by predicting the results of other published experimental procedures. This method has demonstrated how two relatively modest innovations on classical white noise analysis—the inclusion of space as a way to organize response kernels and the use of linear decoupling to measure the response to two channels of visual information simultaneously—could substantially improve our basic understanding of visual processing in the fly. Frontiers Media S.A. 2014-10-31 /pmc/articles/PMC4215624/ /pubmed/25400550 http://dx.doi.org/10.3389/fncir.2014.00130 Text en Copyright © 2014 Aptekar, Keles, Mongeau, Lu, Frye and Shoemaker. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Aptekar, Jacob W. Keles, Mehmet F. Mongeau, Jean-Michel Lu, Patrick M. Frye, Mark A. Shoemaker, Patrick A. Method and software for using m-sequences to characterize parallel components of higher-order visual tracking behavior in Drosophila |
title | Method and software for using m-sequences to characterize parallel components of higher-order visual tracking behavior in Drosophila |
title_full | Method and software for using m-sequences to characterize parallel components of higher-order visual tracking behavior in Drosophila |
title_fullStr | Method and software for using m-sequences to characterize parallel components of higher-order visual tracking behavior in Drosophila |
title_full_unstemmed | Method and software for using m-sequences to characterize parallel components of higher-order visual tracking behavior in Drosophila |
title_short | Method and software for using m-sequences to characterize parallel components of higher-order visual tracking behavior in Drosophila |
title_sort | method and software for using m-sequences to characterize parallel components of higher-order visual tracking behavior in drosophila |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4215624/ https://www.ncbi.nlm.nih.gov/pubmed/25400550 http://dx.doi.org/10.3389/fncir.2014.00130 |
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