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Mouse Academy: high-throughput automated training and trial-by-trial behavioral analysis during learning

Qiao, Mu and Zhang, Tony and Segalin, Cristina and Sam, Sarah and Perona, Pietro and Meister, Markus (2018) Mouse Academy: high-throughput automated training and trial-by-trial behavioral analysis during learning. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20181128-093526738

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Abstract

Progress in understanding how individual animals learn will require high-throughput standardized methods for behavioral training but also advances in the analysis of the resulting behavioral data. In the course of training with multiple trials, an animal may change its behavior abruptly, and capturing such events calls for a trial-by-trial analysis of the animal's strategy. To address this challenge, we developed an integrated platform for automated animal training and analysis of behavioral data. A low-cost and space-efficient apparatus serves to train entire cohorts of mice on a decision-making task under identical conditions. A generalized linear model (GLM) analyzes each animal's performance at single-trial resolution. This model infers the momentary decision-making strategy and can predict the animal's choice on each trial with an accuracy of ~80%. We also introduce automated software to assess the animal's detailed trajectories and body poses within the apparatus. Unsupervised analysis of these features revealed unusual trajectories that represent hesitation in the response. This integrated hardware/software platform promises to accelerate the understanding of animal learning.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/467878DOIDiscussion Paper
https://drive.google.com/open?id=1gkPbqGYKPGs7Rx1WNmubQW0dKyYE5YVRRelated ItemDatasets
ORCID:
AuthorORCID
Qiao, Mu0000-0001-7309-4237
Segalin, Cristina0000-0001-7219-7074
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. All rights reserved. No reuse allowed without permission. We thank Joshua Sanders for technical assistance in incorporating Bpod into our system. We thank Ann Kennedy for insightful comments and suggestions on analysis of the behavioral trajectories. We thank Oisin Mac Adoha and Yuxin Chen for helpful comments and discussions. This work was supported by a grant from the Simons Foundation (SCGB 543015, M.M. and P.P.) and a postdoctoral fellowship from the Swartz Foundation (M.Q.). AUTHOR CONTRIBUTIONS: M.Q. and M.M. designed the study; M.Q. and S.S. constructed the hardware setup and wrote the controlling software; M.Q. performed experiments and collected data for analysis; M.Q. developed the iterative generalized linear model with input from P.P. and M.M; C.S. developed the automated tracking software; T.Z. implemented animal tracking and behavioral trajectory analysis with input from M.Q., P.P. and M.M; M.Q. and M.M. wrote the manuscript with comments from all authors. DATA AVAILABILITY: The datasets analyzed during the current study are available in https://drive.google.com/open?id=1gkPbqGYKPGs7Rx1WNmubQW0dKyYE5YVR The authors declare no competing financial interests.
Funders:
Funding AgencyGrant Number
Simons FoundationSCGB 543015
Swartz FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20181128-093526738
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20181128-093526738
Official Citation:Mouse Academy: high-throughput automated training and trial-by-trial behavioral analysis during learning Mu Qiao, Tony Zhang, Cristina Segalin, Sarah Sam, Pietro Perona, Markus Meister bioRxiv 467878; doi: https://doi.org/10.1101/467878
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:91278
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:28 Nov 2018 18:27
Last Modified:28 Nov 2018 18:27

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