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
Open science: redefining operant conditioning; PKC and motorneurons
1.
2. OPEN NEUROSCIENCE VIA AUTOMATIC PUBLICATION OF
DIGITAL DATA:
FROM LOCOMOTION TO OPERANT "SELF-LEARNING" IN
DROSOPHILA
Julien Colomb
Freie Universität Berlin
6. PLAN
•
World- and self-learning: redefining operant learning
•
PKC, motorneurons and self-learning
•
Open science: philosophy and practice
7. PLAN
•
World- and self-learning: redefining operant learning
•
PKC, motorneurons and self-learning
•
Open science: philosophy and practice
Figshare and Rfigshare
8. 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
9. 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” ?
10. 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
14. 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 ?
37. 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?
40. OPEN SCIENCE BY DEFAULT
Making scientific research, data and dissemination accessible to all levels of an inquiring
society, amateur or professional.
45. 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
53. 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.
60. 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
61. 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
62. 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
63. 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
65. CONCLUSION:
R AND DATA ANALYSIS
1. Graphical representation and statistics
66. CONCLUSION:
R AND DATA ANALYSIS
1. Graphical representation and statistics
2. Reproducible data analysis
67. CONCLUSION:
R AND DATA ANALYSIS
1. Graphical representation and statistics
2. Reproducible data analysis
3. Graphs & data publishable on Figshare
68. 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
69. 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