Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Transition scale-spaces: A computational theory for the discretized entorhinal cortex

View ORCID ProfileNicolai Waniek
doi: https://doi.org/10.1101/543801
Nicolai Waniek
1Bosch Center for Artificial Intelligence (BCAI), Robert Bosch GmbH, Robert-Bosch-Campus 1, 71272, Renningen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Nicolai Waniek
  • For correspondence: nicolai.waniek@de.bosch.com
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Although hippocampal grid cells are thought to be crucial for spatial navigation, their computational purpose remains disputed. Recently, they were proposed to represent spatial transitions and to convey this knowledge downstream to place cells. However, a single scale of transitions is insufficient to plan long goal-directed sequences in behaviorally acceptable time.

Here, a scale-space data structure is suggested to optimally accelerate retrievals from transition systems, called Transition Scale-Space (TSS). Remaining exclusively on an algorithmic level, the scale increment is proved to be ideally Embedded Image for biologically plausible receptive fields. It is then argued that temporal buffering is necessary to learn the scale-space online. Next, two modes for retrieval of sequences from the TSS are presented, namely top-down and bottom-up. The two modes are evaluated in symbolic simulations, i.e., without biologically plausible spiking neurons. Additionally, a TSS is used for short-cut discovery in a simulated Morris water maze. Finally, the presented results are discussed in depth with respect to biological plausibility, and several testable predictions derived. Moreover, relations to other grid cell models, multi-resolution path planning, and scale-space theory are highlighted. Summarized, reward-free transition encoding is shown here, in a theoretical model, to be compatible with the observed discretization along the dorso-ventral axis of medial Entorhinal Cortex (MEC).

Because the theoretical model generalizes beyond navigation, the TSS is suggested to be a general-purpose cortical data structure for fast retrieval of sequences and relational knowledge.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted September 22, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Transition scale-spaces: A computational theory for the discretized entorhinal cortex
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Transition scale-spaces: A computational theory for the discretized entorhinal cortex
Nicolai Waniek
bioRxiv 543801; doi: https://doi.org/10.1101/543801
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Transition scale-spaces: A computational theory for the discretized entorhinal cortex
Nicolai Waniek
bioRxiv 543801; doi: https://doi.org/10.1101/543801

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4684)
  • Biochemistry (10361)
  • Bioengineering (7675)
  • Bioinformatics (26337)
  • Biophysics (13529)
  • Cancer Biology (10686)
  • Cell Biology (15440)
  • Clinical Trials (138)
  • Developmental Biology (8497)
  • Ecology (12821)
  • Epidemiology (2067)
  • Evolutionary Biology (16862)
  • Genetics (11399)
  • Genomics (15478)
  • Immunology (10617)
  • Microbiology (25219)
  • Molecular Biology (10223)
  • Neuroscience (54473)
  • Paleontology (401)
  • Pathology (1668)
  • Pharmacology and Toxicology (2897)
  • Physiology (4342)
  • Plant Biology (9247)
  • Scientific Communication and Education (1586)
  • Synthetic Biology (2558)
  • Systems Biology (6781)
  • Zoology (1466)