10 research outputs found
A polar prediction model for learning to represent visual transformations
All organisms make temporal predictions, and their evolutionary fitness level
depends on the accuracy of these predictions. In the context of visual
perception, the motions of both the observer and objects in the scene structure
the dynamics of sensory signals, allowing for partial prediction of future
signals based on past ones. Here, we propose a self-supervised
representation-learning framework that extracts and exploits the regularities
of natural videos to compute accurate predictions. We motivate the polar
architecture by appealing to the Fourier shift theorem and its group-theoretic
generalization, and we optimize its parameters on next-frame prediction.
Through controlled experiments, we demonstrate that this approach can discover
the representation of simple transformation groups acting in data. When trained
on natural video datasets, our framework achieves better prediction performance
than traditional motion compensation and rivals conventional deep networks,
while maintaining interpretability and speed. Furthermore, the polar
computations can be restructured into components resembling normalized simple
and direction-selective complex cell models of primate V1 neurons. Thus, polar
prediction offers a principled framework for understanding how the visual
system represents sensory inputs in a form that simplifies temporal prediction
Neuromatch Academy: a 3-week, online summer school in computational neuroscience
Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function
LabForComputationalVision/pyrtools: v1.0.2
<p>Small documentation-related updates. The main goal of this release is to trigger Zenodo, so we get a DOI.</p>
<h2>What's Changed</h2>
<ul>
<li>Bug report template by @billbrod in https://github.com/LabForComputationalVision/pyrtools/pull/19</li>
<li>Readthedocs by @billbrod in https://github.com/LabForComputationalVision/pyrtools/pull/22</li>
<li>Update index.rst by @billbrod in https://github.com/LabForComputationalVision/pyrtools/pull/23</li>
<li>Update README.md by @billbrod in https://github.com/LabForComputationalVision/pyrtools/pull/24</li>
</ul>
<p><strong>Full Changelog</strong>: https://github.com/LabForComputationalVision/pyrtools/compare/v1.0.1...v1.0.2</p>
Pyrtools: tools for multi-scale image processing
<p>Largely documentation-related updates:</p>
<h2>What's Changed</h2>
<ul>
<li>Bug report template by @billbrod in https://github.com/LabForComputationalVision/pyrtools/pull/19</li>
<li>Readthedocs by @billbrod in https://github.com/LabForComputationalVision/pyrtools/pull/22</li>
<li>Update index.rst by @billbrod in https://github.com/LabForComputationalVision/pyrtools/pull/23</li>
<li>Update README.md by @billbrod in https://github.com/LabForComputationalVision/pyrtools/pull/24</li>
<li>Release 1.0.2 updates by @billbrod in https://github.com/LabForComputationalVision/pyrtools/pull/25<ul>
<li>adds zenodo badges, citation guide, citation.cff file, and updates <code>1.0.1</code> to <code>1.0.2</code> throughout package.</li>
</ul>
</li>
</ul>
<p><strong>Full Changelog</strong>: https://github.com/LabForComputationalVision/pyrtools/compare/v1.0.1...v1.0.2</p>If you use any component of pyrtools, please cite it as below
LabForComputationalVision/plenoptic: 1.0.2
<p>This release includes some minor changes, largely packaging and documentation related.</p>
<h2>What's Changed</h2>
<ul>
<li>Switch to using pyproject.toml by @billbrod in https://github.com/LabForComputationalVision/plenoptic/pull/215</li>
<li>Update index.rst by @eerosim in https://github.com/LabForComputationalVision/plenoptic/pull/212</li>
<li>Adds citation guide, minor improvements to docs by @billbrod in https://github.com/LabForComputationalVision/plenoptic/pull/219</li>
<li>hotfix: remove use_line_collection by @billbrod in https://github.com/LabForComputationalVision/plenoptic/pull/220</li>
<li>Readthedocs hotfix by @billbrod in https://github.com/LabForComputationalVision/plenoptic/pull/224</li>
<li>Small documentation-related fixes by @billbrod in https://github.com/LabForComputationalVision/plenoptic/pull/226</li>
</ul>
<p><strong>Full Changelog</strong>: https://github.com/LabForComputationalVision/plenoptic/compare/1.0.1...1.0.2</p>