Motor Control and Modeling in Practice

prof.dr. Jaap Murre

University of Amsterdam

University of Maastricht

jaap@murre.com

http://neuromod.org

Motor Control in Mammals

Motor control is very much a cognitive process

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?

Components of the motor system in mammals

• Muscles

• Brain stem

• Cerebellum

• Basal ganglia

• Cortical areas (area 6: motor cortex)

Schematicoverview of themotor system

Simple movement activations motor cortex and somatosensory cortex

More complicated sequences involve other areas

SMA = supplementary motor area (part of area 6)

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

Internally and externally generated movements

PMC = premotor cortex (also part of area 6)

Skilled (Old) versus new motor movements

Summary of the architecture of the motor system

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’

Summary (continued)

• Motor behavior is not simply stringing together some basic movements

• Motor planning and execution are very much cognitive functions

Neural networks and robotics

Robotics

• There are currently almost no completely autonomous robots

• There are currently no autonomous robots that could pass for a human

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

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

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

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

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

Situatedness

• Put the environment into the loop: action, environment, perception

• As opposed to:– Pattern recognition– Motor behavior– Correction on missed targets (darn’

environment!)

Modeling in Practice

Suppose, you want to build a new model...

Steps in modeling

• Where to start

• Choices to be made

• Data

• Simulations

• Fitting or comparison with the data

• Analysis and tinkering

• Reporting and publishing

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

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

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?

Choices to be made: Architecture

• The architecture involves– Number and size of layers or modules– Their gross interconnectivity– Global parameter settings

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

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?

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?

Fitting the data• How do I decide that my simulation performs

adequately?– ‘Eyeballing’ the data– Percentage variance explained (R2 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?

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

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

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