open science: redefining operant conditioning; pkc and motorneurons
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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
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
Place learningResponse 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, non-parametric, higher than 0 ?
SELF-LEARNING ONLYDissecting world- and self-learning
Colomb and Brembs, 2010
DROSOPHILA FLIGHT SIMULATORDissecting 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Mendoza et al., unpublished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 ACTIONPKC inhibition:
only during test only in certain neurons
POSITIVE CONTROLheat shock protocol for the TARGET system using a pan-neuronal Gal4
FIRST SCREENnot 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 PLASTICITYAiko K. Thompson,, Xiang Yang Chen, and Jonathan R. Wolpaw, 2009
HAS THERAPEUTIC APPLICATION IN HUMANThompson AK, Pomerantz FR, Wolpaw JR., 2013
OPEN SCIENCE BY DEFAULTMaking scientific research, data and dissemination accessible to all levels of an inquiring
society, amateur or professional.
BURIDAN’S PARADIGMAssess locomotor behavior
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
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.
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
• 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
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
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
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
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