Decomposing the motor system

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Presentation transcript:

Decomposing the motor system Ben-Gurion University Beer-Sheva Decomposing the motor system Opher Donchin

Noise and learning A bit about noise in the motor system how noise and learning are related Also a bit about Bayesian statistical modeling learning in the motor system A surprisingly simple proposal

Obligatory sports video https://www.youtube.com/embed/8dLx53NanQ0

Motor noise persists in simpler situations Haar J Neurosci 2017

Subjects noisy using one arm are noisy in both Haar J Neurosci 2017

How noisy a subject is to one target predicts how noisy they are to others Haar J Neurosci 2017

(At least) two components of noise are uncorrelated Haar J Neurosci 2017

We can also characterize noise in brain activity Haar J Neurosci 2017

Consistency of variability is stronger contralaterally (in some areas) Haar J Neurosci 2017

For left arm, variability is consistent in right M1 but left PMd or posterior parietal Haar J Neurosci 2017

Consistency of variability across movement of the two arms Haar J Neurosci 2017

Correlation of brain variability to kinematic variability Haar J Neurosci 2017

What have we learned about motor variability? Multiple independent components Consistent variability within component No correlation across components Some motor brain areas show consistent variability in some movements Correlation of behavioral and neural variability in posterior parietal cortex

Ok. One more https://www.youtube.com/embed/S9KE2R92pSg

Variability is correlated to learning Wu et al, Nature Neuroscience, 2014

But not in all experiments He et al, PLOS Computational Biology, 2016

Explanation: different types of noise Learning Learning rate Forgetting Planning noise Behavior Execution noise Kalman filter models predict learning will be: Correlated with planning noise Inversely correlated with execution nosie

Our model of the learning process https://www.biorxiv.org/content/early/2017/12/28/238865

Parameters of the model https://www.biorxiv.org/content/early/2017/12/28/238865

Hierarchical model over subjects https://www.biorxiv.org/content/early/2017/12/28/238865

Noise and learning identified with different epochs No visual feedback https://www.biorxiv.org/content/early/2017/12/28/238865

Sampling from the posterior distribution https://www.biorxiv.org/content/early/2017/12/28/238865

Each sample is a different full set of parameters Chain 1, Sample 8794 Chain 2, Sample 18,836

Samples together reflect shape of posterior distribution

Posterior distributions of population and hyperparameter https://www.biorxiv.org/content/early/2017/12/28/238865

Planning noise correlated with learning https://www.biorxiv.org/content/early/2017/12/28/238865

Execution noise inversely correlated https://www.biorxiv.org/content/early/2017/12/28/238865

Optimal Kalman gain predicts learning https://www.biorxiv.org/content/early/2017/12/28/238865

Where are we now? Part 1 Variability in subjects is consistent Correlation of learning and noise in posterior parietal Part 2 Learning correlated with state noise Inversely with output noise Predicted by Kalman optimum

Last one, I promise https://www.youtube.com/watch?v=51uXMHdamb4

Using MRI to Localize learning Haar J Neurosci 2015

Decoding movement direction from pattern of fMRI activity Haar J Neurosci 2015

Many areas of cortex reflect movement Haar J Neurosci 2015

After adaptation: visual, motor and parietal! Haar J Neurosci 2015

Final Summary Learning predicted by optimal Kalman gain Balancing state and output noise Posterior parietal cortex Noise correlates with behavioral noise Learning changes activity patterns

Thanks! Shlomi Haar Rick Van Der Vliet Ruud Selles, Maarten Frens and Ilan Dinstein Co-supervision

Sorry, I lied! https://www.youtube.com/watch?v=Hmjv9KxCzZU

We can also simulate behavior

Simul

Fig 3 D-G,I: Model parameters and measurable values, results

Parietal areas are motor-colored visual representation Haar J Neurosci 2015