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

2. OPEN NEUROSCIENCE VIA AUTOMATIC PUBLICATION OF
DIGITAL DATA:
FROM LOCOMOTION TO OPERANT "SELF-LEARNING" IN
DROSOPHILA
Julien Colomb
Freie Universität Berlin

3. PLAN

4. PLAN
•
World- and self-learning: redefining operant learning

5. PLAN
•
World- and self-learning: redefining operant learning
•
PKC, motorneurons and self-learning

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

12. Response learning
Place learning

13. METHOD
Brembs and Plendel, 2008

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 ?

15. SELF-LEARNING ONLY
Dissecting world- and self-learning
Colomb and Brembs, 2010

16. DROSOPHILA FLIGHT SIMULATOR
Dissecting world- and self-learning
Colomb and Brembs, 2010

19. Mendoza et al., unpublished

20. THE WHAT AND
WHERE OF
SELF-LEARNING
•
Which PKC is involved
•
In which neurons is PKC
involved

21. GENETIC TOOLS

22. UAS-GAL4 SYSTEM:
SPATIAL AND TEMPORAL
CONTROL

23. UAS-GAL4 SYSTEM:
SPATIAL AND TEMPORAL
CONTROL

24. UAS-GAL4 SYSTEM:
SPATIAL AND TEMPORAL
CONTROL
•
PKCi

25. UAS-GAL4 SYSTEM:
SPATIAL AND TEMPORAL
CONTROL
•
PKCi
•
RNAi

26. RESULTS

27. WHICH PKC ?
No conclusive results

28. LOCALISATION OF PKC ACTION
PKC inhibition:
only during test
only in certain neurons

29. POSITIVE CONTROL
heat shock protocol for the TARGET system using a pan-neuronal Gal4

30. FIRST SCREEN
not in central brain, in glutamatergic neurons

31. MOTORNEURONS

33. ANATOMICAL CONFIRMATION:
IN PROGRESS
Gal4 lines crossed to a UAS-CD8GFP
antibody staining: anti-GFP , anti-dvGlut

34. DISCUSSION

35. DISCUSSION
•
Motorneurons as probable site of plasticity for self-learning

36. DISCUSSION
•
Motorneurons as probable site of plasticity for self-learning
•
Interaction self-/world-learning: probably different neuronal site

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?

38. INVOLVES MOTORNEURON INTRINSIC PLASTICITY
Aiko K. Thompson,, Xiang Yang Chen, and Jonathan R. Wolpaw, 2009

39. HAS THERAPEUTIC APPLICATION IN HUMAN
Thompson AK, Pomerantz FR, Wolpaw JR., 2013

40. OPEN SCIENCE BY DEFAULT
Making scientific research, data and dissemination accessible to all levels of an inquiring
society, amateur or professional.

44. BURIDAN’S PARADIGM
Assess locomotor behavior

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

46. DIFFERENT SUB-STRAINS OF CS
(WILD TYPE) FLIES.

47. DIFFERENT SUB-STRAINS OF CS
(WILD TYPE) FLIES.

48. DIFFERENT SUB-STRAINS OF CS
(WILD TYPE) FLIES.

49. CENTROID TRAJECTORY ANALYSIS

50. CENTROID TRAJECTORY ANALYSIS
Automatic publication

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.

54. Rfigshare from Ropensci team
http://ropensci.org/ :
!
2013 RopenSci challenge

55. DIFFICULTIES
•
Metadata format: include more types of trajectory data
•
Is Figshare the right platform for this, wouldn't be a git based solution
better?

56. OPEN SCIENCE AND
THE SELF-LEARNING SETUP

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

64. CONCLUSION:
R AND DATA ANALYSIS

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