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Open science: redefining operant conditioning; PKC and motorneurons
 

Open science: redefining operant conditioning; PKC and motorneurons

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Presentation introducing the need for a new definition of operant conditioning, and presenting data suggesting an action of PKC in motorneurons during self-learning in Drosophila. Finally, some slides ...

Presentation introducing the need for a new definition of operant conditioning, and presenting data suggesting an action of PKC in motorneurons during self-learning in Drosophila. Finally, some slides about our attempt in working using open science as a default mode

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    Open science: redefining operant conditioning; PKC and motorneurons Open science: redefining operant conditioning; PKC and motorneurons Presentation Transcript

    • OPEN NEUROSCIENCE VIA AUTOMATIC PUBLICATION OF DIGITAL DATA:
 FROM LOCOMOTION TO OPERANT "SELF-LEARNING" IN DROSOPHILA Julien Colomb Freie Universität Berlin
    • PLAN
    • PLAN • World- and self-learning: redefining operant learning
    • PLAN • World- and self-learning: redefining operant learning • PKC, motorneurons and self-learning
    • PLAN • World- and self-learning: redefining operant learning • PKC, motorneurons and self-learning • Open science: philosophy and practice
    • PLAN • World- and self-learning: redefining operant learning • PKC, motorneurons and self-learning • Open science: philosophy and practice Figshare and Rfigshare
    • PLAN • World- and self-learning: redefining operant learning • PKC, motorneurons and self-learning • Open science: philosophy and practice Figshare and Rfigshare Locomotion data and self-learning data
    • OPERANT CONDITIONING: DISSOCIABLE LEARNING TYPES “A process of behavior modification in which the likelihood of a specific behavior is increased or decreased through positive or negative reinforcement” ?
    • OPERANT CONDITIONING: DISSOCIABLE LEARNING TYPES “A process of behavior modification in which the likelihood of a specific behavior is increased or decreased through positive or negative reinforcement” ? Tolman, 1946
    • Response learning Place learning
    • METHOD Brembs and Plendel, 2008
    • PROTOCOL • 7 blocks of 2 minutes • PI = proportion of time spent performing the “safe” behavior • self-learning assessed during the last test period • statistics = for each group, nonparametric, higher than 0 ?
    • SELF-LEARNING ONLY Dissecting world- and self-learning Colomb and Brembs, 2010
    • DROSOPHILA FLIGHT SIMULATOR Dissecting world- and self-learning Colomb and Brembs, 2010
    • Mendoza et al., unpublished
    • THE WHAT AND WHERE OF 
 SELF-LEARNING • Which PKC is involved • In which neurons is PKC involved
    • GENETIC TOOLS
    • UAS-GAL4 SYSTEM: SPATIAL AND TEMPORAL CONTROL
    • UAS-GAL4 SYSTEM: SPATIAL AND TEMPORAL CONTROL
    • UAS-GAL4 SYSTEM: SPATIAL AND TEMPORAL CONTROL • PKCi
    • UAS-GAL4 SYSTEM: SPATIAL AND TEMPORAL CONTROL • PKCi • RNAi
    • RESULTS
    • WHICH PKC ? No conclusive results
    • LOCALISATION OF PKC ACTION PKC inhibition: only during test only in certain neurons
    • POSITIVE CONTROL heat shock protocol for the TARGET system using a pan-neuronal Gal4
    • FIRST SCREEN not in central brain, in glutamatergic neurons
    • MOTORNEURONS
    • ANATOMICAL CONFIRMATION: IN PROGRESS Gal4 lines crossed to a UAS-CD8GFP antibody staining: anti-GFP , anti-dvGlut
    • DISCUSSION
    • DISCUSSION • Motorneurons as probable site of plasticity for self-learning
    • DISCUSSION • Motorneurons as probable site of plasticity for self-learning • Interaction self-/world-learning: probably different neuronal site
    • DISCUSSION • Motorneurons as probable site of plasticity for self-learning • Interaction self-/world-learning: probably different neuronal site • Then why different molecular substrate? Different cellular correlates?
    • INVOLVES MOTORNEURON INTRINSIC PLASTICITY Aiko K. Thompson,, Xiang Yang Chen, and Jonathan R. Wolpaw, 2009
    • HAS THERAPEUTIC APPLICATION IN HUMAN Thompson AK, Pomerantz FR, Wolpaw JR., 2013
    • OPEN SCIENCE BY DEFAULT Making scientific research, data and dissemination accessible to all levels of an inquiring society, amateur or professional.
    • BURIDAN’S PARADIGM Assess locomotor behavior
    • 12 VARIABLES CALCULATED Median speed Speed of the animal while walking (median) Mean distance travelled Distance travelled during the experiment divided by the length of the experiment. Turning angle median of the angle difference between two movement Meander median of the turning angle divided by instantaneous speed thigmotaxis while moving proportion of time spent moving on the edge of the platform versus the center of the platform (equal surfaces) proportion of time spent not moving on the edge of the platform versus the center of the thigmotaxis while sitting platform (equal surfaces) Stripe deviation Median deviation angle between walking direction and direction toward the stripes Number of walks number of times a fly walk between the two stripes during the experiment number of pauses number of times a fly made a pause (longer than 1s) during the experiment activity bouts duration Median length of activity phases pause length Median length of pauses total time active sum of the length of activity phases during the experiment
    • DIFFERENT SUB-STRAINS OF CS 
 (WILD TYPE) FLIES.
    • DIFFERENT SUB-STRAINS OF CS 
 (WILD TYPE) FLIES.
    • DIFFERENT SUB-STRAINS OF CS 
 (WILD TYPE) FLIES.
    • CENTROID TRAJECTORY ANALYSIS
    • CENTROID TRAJECTORY ANALYSIS Automatic publication
    • API The figshare API allows you to push data to figshare, or pull data out. This first version is a basic implementation that allows you to manage your figshare account or build applications on top of the figshare platform and public research.
    • Rfigshare from Ropensci team http://ropensci.org/ : ! 2013 RopenSci challenge
    • DIFFICULTIES • Metadata format: include more types of trajectory data • Is Figshare the right platform for this, wouldn't be a git based solution better?
    • OPEN SCIENCE AND
 THE SELF-LEARNING SETUP
    • DATA PUBLICATION • Get all data on the same format • all results in one file • link metadata and raw torque data • Publish on Figshare http://dx.doi.org/10.6084/m9.figshare.830423
    • DATA PUBLICATION • Get all data on the same format • all results in one file • link metadata and raw torque data • Publish on Figshare http://dx.doi.org/10.6084/m9.figshare.830423
    • DATA PUBLICATION One metadata
 file • Get all data on the same format • all results in one file • link metadata and raw torque data • Publish on Figshare http://dx.doi.org/10.6084/m9.figshare.830423
    • DATA PUBLICATION One metadata
 file • Get all data on the same format • all results in one file • link metadata and raw torque data • Publish on Figshare http://dx.doi.org/10.6084/m9.figshare.830423
    • CONCLUSION: 
 R AND DATA ANALYSIS
    • CONCLUSION: 
 R AND DATA ANALYSIS 1. Graphical representation and statistics
    • CONCLUSION: 
 R AND DATA ANALYSIS 1. Graphical representation and statistics 2. Reproducible data analysis
    • CONCLUSION: 
 R AND DATA ANALYSIS 1. Graphical representation and statistics 2. Reproducible data analysis 3. Graphs & data publishable on Figshare
    • CONCLUSION: 
 R AND DATA ANALYSIS 1. Graphical representation and statistics 2. Reproducible data analysis 3. Graphs & data publishable on Figshare 4. Automatic publication/archivage of the data and results, during analysis
    • ACKNOWLEDGMENTS Direct collaborators: Bjoern Brembs Axel Gorostiza ! Reagents, machine, software and flies: M. Heisenberg, H. Aberle, C. Duch, T. Preat, H. Scholz, J. Wessnitzer, T. Colomb, S. Sigrist, B.v.Swinderen. FoxP project: H.J. Pflüger, C. Scharff, A. Mendoza, T. Zars