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      Value-complexity tradeoff explains mouse navigational learning

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          Abstract

          We introduce a novel methodology for describing animal behavior as a tradeoff between value and complexity, using the Morris Water Maze navigation task as a concrete example. We develop a dynamical system model of the Water Maze navigation task, solve its optimal control under varying complexity constraints, and analyze the learning process in terms of the value and complexity of swimming trajectories. The value of a trajectory is related to its energetic cost and is correlated with swimming time. Complexity is a novel learning metric which measures how unlikely is a trajectory to be generated by a naive animal. Our model is analytically tractable, provides good fit to observed behavior and reveals that the learning process is characterized by early value optimization followed by complexity reduction. Furthermore, complexity sensitively characterizes behavioral differences between mouse strains.

          Author summary

          Goal directed learning typically involves the computation of complex sequences of actions. However, computational frameworks such as reinforcement learning focus on optimizing the reward, or value, associated with action sequences while ignoring their complexity cost. Here we develop a complexity-limited optimal control model of the Morris Water Maze navigation task: a widely used tool for characterizing the effects of genetic and other experimental manipulations in animal models. Our proposed complexity metric provides new insights on the dynamics of navigational learning and reveals behavioral differences between mouse strains.

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          On Information and Sufficiency

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            A New Approach to Linear Filtering and Prediction Problems

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              Morris water maze: procedures for assessing spatial and related forms of learning and memory.

              The Morris water maze (MWM) is a test of spatial learning for rodents that relies on distal cues to navigate from start locations around the perimeter of an open swimming arena to locate a submerged escape platform. Spatial learning is assessed across repeated trials and reference memory is determined by preference for the platform area when the platform is absent. Reversal and shift trials enhance the detection of spatial impairments. Trial-dependent, latent and discrimination learning can be assessed using modifications of the basic protocol. Search-to-platform area determines the degree of reliance on spatial versus non-spatial strategies. Cued trials determine whether performance factors that are unrelated to place learning are present. Escape from water is relatively immune from activity or body mass differences, making it ideal for many experimental models. The MWM has proven to be a robust and reliable test that is strongly correlated with hippocampal synaptic plasticity and NMDA receptor function. We present protocols for performing variants of the MWM test, from which results can be obtained from individual animals in as few as 6 days.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Investigation
                Role: Data curationRole: Investigation
                Role: Funding acquisitionRole: InvestigationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                December 2020
                11 December 2020
                : 16
                : 12
                : e1008497
                Affiliations
                [1 ] Edmond and Lily Safra Center for Brain Sciences (ELSC), Hebrew University, Jerusalem, Israel
                [2 ] The Alexander Silberman Institute of Life Sciences, Hebrew University, Jerusalem, Israel
                [3 ] The Benin School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel
                University of Toronto at Scarborough, CANADA
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-7207-1559
                https://orcid.org/0000-0002-1728-8114
                https://orcid.org/0000-0003-4071-5361
                https://orcid.org/0000-0002-6645-107X
                Article
                PCOMPBIOL-D-20-00247
                10.1371/journal.pcbi.1008497
                7758052
                33306669
                b63944fa-a8b9-4d70-94a4-5ded3d9a6cbe
                © 2020 Amir et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 February 2020
                : 6 November 2020
                Page count
                Figures: 7, Tables: 0, Pages: 31
                Funding
                This study was supported by Advanced European Research Council (ERC) grant 340063 (project RATLAND; https://erc.europa.eu/) and F.I.R.S.T. grant 1075/13 to IN, and by a personal grant from the Israel Science Foundation ( https://www.isf.org.il/) grant no. 575/17 to SS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Rodents
                Mice
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Amniotes
                Mammals
                Rodents
                Mice
                Biology and Life Sciences
                Physiology
                Biological Locomotion
                Swimming
                Biology and Life Sciences
                Psychology
                Behavior
                Animal Behavior
                Social Sciences
                Psychology
                Behavior
                Animal Behavior
                Biology and Life Sciences
                Zoology
                Animal Behavior
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Learning
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Learning
                Social Sciences
                Psychology
                Cognitive Psychology
                Learning
                Biology and Life Sciences
                Neuroscience
                Learning and Memory
                Learning
                Physical Sciences
                Physics
                Classical Mechanics
                Motion
                Velocity
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Model Organisms
                Mouse Models
                Research and Analysis Methods
                Model Organisms
                Mouse Models
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Animal Models
                Mouse Models
                Physical Sciences
                Physics
                Thermodynamics
                Free Energy
                Engineering and Technology
                Industrial Engineering
                Control Engineering
                Control Theory
                Computer and Information Sciences
                Systems Science
                Control Theory
                Physical Sciences
                Mathematics
                Systems Science
                Control Theory
                Custom metadata
                vor-update-to-uncorrected-proof
                2020-12-23
                The numerical data of all swimming trajectories, as well as Matlab files for generating the figures are available on the Open Science Framework repository, at https://osf.io/3wgzx/ or DOI 10.17605/OSF.IO/3WGZX.

                Quantitative & Systems biology
                Quantitative & Systems biology

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