1. Motor Control and Modeling in Practice prof.dr. Jaap Murre University of Amsterdam University of Maastricht jaap@murre.com http://neuromod.org

2. Motor Control in Mammals Motor control is very much a cognitive process

3. Basic questions regarding motor control can nowadays be answered How are motor movements represented in the brain? How are they used in the production of movement? Which brain areas are involved?

4. Components of the motor system in mammals Muscles Brain stem Cerebellum Basal ganglia Cortical areas (area 6: motor cortex)

5. Schematic overview of the motor system

6. Simple movement activations motor cortex and somatosensory cortex

7. More complicated sequences involve other areas SMA = supplementary motor area (part of area 6)

8. Imagined movements remain limited to the supplementary motor area (SMA)

9. Internally and externally generated movements PMC = premotor cortex (also part of area 6)

10. Skilled (Old) versus new motor movements

11. Summary of the architecture of the motor system

12. Summary Like vision, motor behavior has a lot of special purpose circuitry We can understand many aspects of this circuitry in terms of ‘why this representation makes sense’

13. Summary (continued) Motor behavior is not simply stringing together some basic movements Motor planning and execution are very much cognitive functions

14. Neural networks and robotics

15. Robotics There are currently almost no completely autonomous robots There are currently no autonomous robots that could pass for a human

16. Imitation learning Imitation learning: –Generate random actions –Observe the effect –Learn the relationship between action and effect (perception) and between effect (goal) and action (realization) A model for motor development –Speech: babbling –Motor babbling

17. Kuperstein’s Robot Arm (1988) Input: two cameras Output: one robot arm (three degrees of freedom) Goal: reach for a white ball Problem: How to go from the images of the the two cameras to the correct joint angles –Must learn stereovision –Must solve vision to motor mapping Answer: Motor babbling

18. Mike Jordan’s criticism Kuperstein’s model does not converge Different joint settings give rise to the same joint (stereo) image: One input (stereo image) is thus mapped onto different outputs This cannot converge The inverse kinematics problem is thus not completely solved by this approach

19. Solution: ‘Elastic constraints’ in motor development The problem of grasping is overdetermined: given an end-location, many possible joint positions solve the problem In order to make the problem soluble ‘elastic constraints’ are necessary Muscles (as ‘springs’) are one source of such constraints

20. Rodney Brooks Studied insects and built robot models of them Now humans (skipped frogs, cats, etc.) Again starts with the simplest human behavior: facial and bodily expressions Subsumption architecture Complex behavior emerges through cooperating, but independent layers in interaction with a complex environment

21. Situatedness Put the environment into the loop: action, environment, perception As opposed to: –Pattern recognition –Motor behavior –Correction on missed targets (darn’ environment!)

22. Modeling in Practice Suppose, you want to build a new model...

23. Steps in modeling Where to start Choices to be made Data Simulations Fitting or comparison with the data Analysis and tinkering Reporting and publishing

24. Where to start: sophistication Existence proof model –I will prove that it is in fact possible to implement a model that does X given these data and other constraints Qualitative summary model –My model can concisely describe all phenomena of type X and it predicts Y Quantitative predictive model –My model can quantitatively describe X and it predicts in quantitative detail Y

25. Where to start: area and level Which area of interest? –Vision and attention –Learning and memory –Practical application, e.g., robotics or face recognition Which level? –Neuron level –Neural systems level –Behavioral level

26. Choices to be made: Paradigm Which neural network paradigm? –Does your network require learning? –Supervised or unsupervised? –Does the network have to be biologically or psychological plausible? –Do the data consist of sequential patterns? –Do the data include response times?

27. Choices to be made: Architecture The architecture involves –Number and size of layers or modules –Their gross interconnectivity –Global parameter settings

28. Choices to be made: Pattern Representation Input pattern coding –Binary or continuous –Localized or distributed –Thermometer coding or Gaussian bubble Output pattern coding –Deterministic or probabilistic response –Winner-take-all or other transformation –Other mapping of output to responses

29. Data At what level do I have data available? –Behavioral (reaction times, probability correct) –Macroscopic neural (e.g., fMRI/Lesion data) –Microscopic neural (e.g., single cell recording) Do the data involve real-time interactions? –Will data become available incrementally? –Does my network have to influence its environment? Is the data very noisy or controversial?

30. Simulations How to translate real-world situations or experiments into simulations? –First create ‘artificial subjects’? –Learning and testing phase? –Generalization (predictive) phase? –Brain damaged (lesioned) phase? –Which parameters change during each phase?

31. Fitting the data How do I decide that my simulation performs adequately? –‘Eyeballing’ the data –Percentage variance explained (R 2 measure) –Chi-square statistic also takes into account degrees of freedom –Newer forms of fitting (BIC etc.) also penalize the ‘flexibility’ of a model –What are the free parameters? –And what does a good fit mean?

32. Analysis and tinkering Help, my model works, but why? –Hidden layer analysis (multi-dimensional scaling, receptive field analysis) –Lesion studies and sensitivity analysis (which contributions are essential) Help, my model does not work, why not? –Scale down the simulation and architecture and try to understand the behavior online –Try to predict its behavior in detail and verify

33. Reporting and publishing Which journal to target? –Neural network journals –Psychology or neurobiology –Artificial intelligence –Other fields (neurology, psychiatry, etc.) How many simulations is enough? How much detail should I report? Give ‘Mickey Mouse’ diagrams before real simulations

34. Final remarks Neural network models still do not have fixed standards (be prepared for sometimes very weird reviews) In some fields, they are still considered as a new and somewhat suspicious technique (e.g., psychiatry and neurology) Stay alert for new and exciting possibilities such as neural network models of fMRI data and of genetically informed data