Untangling the Computational Manifold: Connecting Visual Processing to Topological Space

Published on May 31, 2023

Imagine unraveling a tangled ball of yarn, except this time it’s not yarn, it’s the manifold associated with object recognition in the brain. Scientists believe that the brain uses a process called cortically local subspace untangling to make sense of different object categories. They theorize that by untangling these manifolds, the brain can better recognize objects. This process is similar to the kernel trick used in metric space. However, researchers now propose a more general solution: untangling the manifold in the topological space without artificially defining any distance metric. This approach involves either embedding a manifold in a higher-dimensional space to enhance selectivity or flattening it to increase tolerance. In this study, experts explore both strategies and their links to untangling image, audio, and language data. Additionally, they discuss how untangling the manifold could impact motor control and internal representations. Discover the fascinating world of computational untangling! Read the full article for more details.

It has been hypothesized that the ventral stream processing for object recognition is based on a mechanism called cortically local subspace untangling. A mathematical abstraction of object recognition by the visual cortex is how to untangle the manifolds associated with different object categories. Such a manifold untangling problem is closely related to the celebrated kernel trick in metric space. In this paper, we conjecture that there is a more general solution to manifold untangling in the topological space without artificially defining any distance metric. Geometrically, we can either embed a manifold in a higher-dimensional space to promote selectivity or flatten a manifold to promote tolerance. General strategies of both global manifold embedding and local manifold flattening are presented and connected with existing work on the untangling of image, audio, and language data. We also discuss the implications of untangling the manifold into motor control and internal representations.

Read Full Article (External Site)

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes:

<a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>