open science: redefining operant conditioning; pkc and motorneurons
TRANSCRIPT
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OPEN NEUROSCIENCE VIA AUTOMATIC PUBLICATION OF DIGITAL DATA:
FROM LOCOMOTION TO OPERANT "SELF-LEARNING" IN DROSOPHILA
Julien Colomb Freie Universität Berlin
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PLAN
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PLAN• World- and self-learning: redefining operant learning
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PLAN• World- and self-learning: redefining operant learning
• PKC, motorneurons and self-learning
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PLAN• World- and self-learning: redefining operant learning
• PKC, motorneurons and self-learning
• Open science: philosophy and practice
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PLAN• World- and self-learning: redefining operant learning
• PKC, motorneurons and self-learning
• Open science: philosophy and practice
Figshare and Rfigshare
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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
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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” ?
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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
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Place learningResponse learning
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METHOD
Brembs and Plendel, 2008
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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, non-parametric, higher than 0 ?
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SELF-LEARNING ONLYDissecting world- and self-learning
Colomb and Brembs, 2010
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DROSOPHILA FLIGHT SIMULATORDissecting world- and self-learning
Colomb and Brembs, 2010
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Mendoza et al., unpublished
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THE WHAT AND WHERE OF
SELF-LEARNING
• Which PKC is involved
• In which neurons is PKC involved
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GENETIC TOOLS
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UAS-GAL4 SYSTEM: SPATIAL AND TEMPORAL
CONTROL
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UAS-GAL4 SYSTEM: SPATIAL AND TEMPORAL
CONTROL
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UAS-GAL4 SYSTEM: SPATIAL AND TEMPORAL
CONTROL• PKCi
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UAS-GAL4 SYSTEM: SPATIAL AND TEMPORAL
CONTROL• PKCi
• RNAi
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RESULTS
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WHICH PKC ?No conclusive results
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LOCALISATION OF PKC ACTIONPKC inhibition:
only during test only in certain neurons
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POSITIVE CONTROLheat shock protocol for the TARGET system using a pan-neuronal Gal4
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FIRST SCREENnot in central brain, in glutamatergic neurons
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MOTORNEURONS
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ANATOMICAL CONFIRMATION: IN PROGRESS
Gal4 lines crossed to a UAS-CD8GFP antibody staining: anti-GFP , anti-dvGlut
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DISCUSSION
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DISCUSSION
• Motorneurons as probable site of plasticity for self-learning
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DISCUSSION
• Motorneurons as probable site of plasticity for self-learning
• Interaction self-/world-learning: probably different neuronal site
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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?
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INVOLVES MOTORNEURON INTRINSIC PLASTICITYAiko K. Thompson,, Xiang Yang Chen, and Jonathan R. Wolpaw, 2009
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HAS THERAPEUTIC APPLICATION IN HUMANThompson AK, Pomerantz FR, Wolpaw JR., 2013
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OPEN SCIENCE BY DEFAULTMaking scientific research, data and dissemination accessible to all levels of an inquiring
society, amateur or professional.
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BURIDAN’S PARADIGMAssess locomotor behavior
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12 VARIABLES CALCULATEDMedian 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)
thigmotaxis while sitting proportion of time spent not moving on the edge of the platform versus the center of the platform (equal surfaces)
Stripe deviation Median deviation angle between walking direction and direction toward the stripesNumber 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 experimentactivity bouts duration Median length of activity phases
pause length Median length of pausestotal time active sum of the length of activity phases during the experiment
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DIFFERENT SUB-STRAINS OF CS (WILD TYPE) FLIES.
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DIFFERENT SUB-STRAINS OF CS (WILD TYPE) FLIES.
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DIFFERENT SUB-STRAINS OF CS (WILD TYPE) FLIES.
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CENTROID TRAJECTORY ANALYSIS
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CENTROID TRAJECTORY ANALYSIS
Automatic publication
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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.
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DIFFICULTIES
• Metadata format: include more types of trajectory data
• Is Figshare the right platform for this, wouldn't be a git based solution better?
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OPEN SCIENCE AND THE SELF-LEARNING SETUP
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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
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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
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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
One metadatafile
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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
One metadatafile
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CONCLUSION: R AND DATA ANALYSIS
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CONCLUSION: R AND DATA ANALYSIS
1. Graphical representation and statistics
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CONCLUSION: R AND DATA ANALYSIS
1. Graphical representation and statistics
2. Reproducible data analysis
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CONCLUSION: R AND DATA ANALYSIS
1. Graphical representation and statistics
2. Reproducible data analysis
3. Graphs & data publishable on Figshare
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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
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ACKNOWLEDGMENTSDirect 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