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Endotaxis: A Universal Algorithm for Mapping, Goal-Learning, and Navigation

Zhang, Tony and Rosenberg, Matthew and Perona, Pietro and Meister, Markus (2021) Endotaxis: A Universal Algorithm for Mapping, Goal-Learning, and Navigation. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210929-162922601

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Abstract

An animal entering a new environment typically faces three challenges: explore the space for resources, memorize their locations, and navigate towards those targets as needed. Experimental work on exploration, mapping, and navigation has mostly focused on simple environments – such as an open arena, a pond [1], or a desert [2] – and much has been learned about neural signals in diverse brain areas under these conditions [3, 4]. However, many natural environments are highly constrained, such as a system of burrows, or of paths through the underbrush. More generally, many cognitive tasks are equally constrained, allowing only a small set of actions at any given stage in the process. Here we propose an algorithm that learns the structure of an arbitrary environment, discovers useful targets during exploration, and navigates back to those targets by the shortest path. It makes use of a behavioral module common to all motile animals, namely the ability to follow an odor to its source [5]. We show how the brain can learn to generate internal “virtual odors” that guide the animal to any location of interest. This endotaxis algorithm can be implemented with a simple 3-layer neural circuit using only biologically realistic structures and learning rules. Several neural components of this scheme are found in brains from insects to humans. Nature may have evolved a general mechanism for search and navigation on the ancient backbone of chemotaxis.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2021.09.24.461751DOIDiscussion Paper
https://github.com/tonyzhang25/Zhang-2021-EndotaxisRelated ItemCode
ORCID:
AuthorORCID
Perona, Pietro0000-0002-7583-5809
Meister, Markus0000-0003-2136-6506
Additional Information: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. This version posted September 25, 2021. This work was supported by the Simons Collaboration on the Global Brain (grant 543015 to MM and 543025 to PP), by NSF award 1564330 to PP, and by a gift from Google to PP. Data and code availability: Data and code to reproduce the reported results are available at https://github.com/tonyzhang25/Zhang-2021-Endotaxis. Following acceptance of the manuscript they will be archived in a permanent public repository. Author contributions: Conception of the study TZ, MR, PP, MM; Numerical work TZ, PP; Analytical work MM; Drafting the manuscript MM; Revision and approval TZ, MR, PP, MM. Competing interests: The authors declare no competing interests. Colleagues: We thank Kyu Hyun Lee and Ruben Portugues for comments.
Funders:
Funding AgencyGrant Number
Simons Foundation543015
Simons Foundation543025
NSFIIS-1564330
GoogleUNSPECIFIED
DOI:10.1101/2021.09.24.461751
Record Number:CaltechAUTHORS:20210929-162922601
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210929-162922601
Official Citation:Endotaxis: A Universal Algorithm for Mapping, Goal-Learning, and Navigation. Tony Zhang, Matthew Rosenberg, Pietro Perona, Markus Meister. bioRxiv 2021.09.24.461751; doi: https://doi.org/10.1101/2021.09.24.461751
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111089
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:29 Sep 2021 18:37
Last Modified:16 Nov 2021 19:43

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