Limitations of neural map topography for decoding spatial information

Avitan, Lilach, Pujic, Zac, Hughes, Nicholas J., Scott, Ethan K. and Goodhill, Geoffrey J. (2016) Limitations of neural map topography for decoding spatial information. Journal of Neuroscience, 36 19: 5385-5396. doi:10.1523/JNEUROSCI.0385-16.2016

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Author Avitan, Lilach
Pujic, Zac
Hughes, Nicholas J.
Scott, Ethan K.
Goodhill, Geoffrey J.
Title Limitations of neural map topography for decoding spatial information
Journal name Journal of Neuroscience   Check publisher's open access policy
ISSN 1529-2401
Publication date 2016-05-11
Year available 2016
Sub-type Article (original research)
DOI 10.1523/JNEUROSCI.0385-16.2016
Open Access Status File (Publisher version)
Volume 36
Issue 19
Start page 5385
End page 5396
Total pages 12
Place of publication Washington, DC United States
Publisher Society for Neuroscience
Language eng
Subject 2800 Neuroscience
Abstract Topographic maps are common throughout the nervous system, yet their functional role is still unclear. In particular, whether they are necessary for decoding sensory stimuli is unknown. Here we examined this question by recording population activity at the cellular level from the larval zebrafish tectum in response to visual stimuli at three closely spaced locations in the visual field. Due to map imprecision, nearby stimulus locations produced intermingled tectal responses, and decoding based on map topography yielded an accuracy of only 64%. In contrast, maximum likelihood decoding of stimulus location based on the statistics of the evoked activity, while ignoring any information about the locations of neurons in the map, yielded an accuracy close to 100%. A simple computational model of the zebrafish visual system reproduced these results. Although topography is a useful initial decoding strategy, we suggest it may be replaced by better methods following visual experience.
Keyword Computational model
Sensory decoding
Topographic map
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID DP150101152
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
HERDC Pre-Audit
Queensland Brain Institute Publications
School of Biomedical Sciences Publications
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