GitHub - ModelDBRepository/260210: Hierarchical anti-Hebbian network model for the formation of spatial cells in 3D (Soman et al 2019)

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ellipsoid_opt_fit.m		ellipsoid_opt_fit.m
elongation_index.mat		elongation_index.mat
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fcc_gscore.m		fcc_gscore.m
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readme.html		readme.html
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<html>
<p>This is the readme for the MATLAB models associated with the paper:
</p>
<p>Karthik Soman, Srinivasa Chakravarthy, Michael M Yartsev, "A
hierarchical anti-Hebbian network model for the formation of spatial
cells in three-dimensional space", Nature Communications (2018) 9:4046
DOI: <a href="http://doi.org/10.1038/s41467-018-06441-5">10.1038/s41467-018-06441-5</a>
</p>
<p>This model was contributed by Karthik Soman.
</p>
<p>This model shows how spatial representations in 3D space could emerge
using unsupervised neural networks. Model is a hierarchical one which
means that it has multiple layers, where each layer has a specific
function to achieve. This architecture is more of a generalised one
i.e. it gives rise to different kinds of spatial representations after
training.
</p>
Usage:<br/>
------<br/>
<p>After extracting this archive cd to the new folder and type
"main_code" at the matlab command prompt. After a minute or two, 28
figures should appear including these:
</p>
<img src="./images/screenshot.png" alt="screenshot 1" width="550"><p/>
<img src="./images/screenshot2.png" alt="screenshot 2" width="550"><p/>
<img src="./images/screenshot3.png" alt="screenshot 3" width="550"><p/>
  
</html>