Alex Cayco Gajic Journal Club 10/29/13

1. Alex Cayco Gajic Journal Club 10/29/13

2. How does motor cortex generate muscle activity? • Representational perspective: • Muscle activity or abstract trajectory parameters? (e.g. hand velocity) • Focus on ‘code’ in single neurons

3. Criticism of the representational approach An epic, twenty-year battle was fought over the cortical representation of movement. Do motor cortex neurons represent the direction of the hand during reaching, or do they represent other features of movement such as joint rotation or muscle output? Graziano 2011 The role of the motor system is to produce movement, not to describe it. Cisek 2006

4. How does motor cortex generate muscle activity? • Representational perspective: • Muscle activity or abstract trajectory parameters? (e.g. hand velocity) • Focus on ‘code’ in single neurons • Dynamical systems perspective: • How can cortex flexibly generate such a large repertoire of movements? • Focus on basis sets/ mixed signals, network properties

5. A few equations • Representational view: • rn(t) = fn(param1(t),param2(t),…) • Dynamical systems perspective: • Neural responses  muscle movement: • m(t) = G[r(t)] • But dimensionality of m << dimensionality of r, soG is probably not invertible • Dynamical system for population activity: • τr’(t) = h(r(t)) + u(t) • Dimensionality reduction techniques will be important to find robust, redundant activity patterns

6. Voluntary movements are “prepared” Churchland et al. 2006 Random delay period

7. Voluntary movements are “prepared” • RT decreases with delay period, indicating “preparation” Churchland et al. 2006

8. Voluntary movements are “prepared” • RT decreases with delay period, indicating “preparation” • Variety of complex single-neuron responses Churchland et al. 2006

9. What is preparatory activity?Representational view • Hypothesis: preparatory activity is the subthreshold form of movement activity Churchland et al. 2010a

10. What is preparatory activity?Representational view • Hypothesis: preparatory activity is the subthreshold form of movement activity Churchland et al. 2010a

11. What is preparatory activity?Representational view • Hypothesis: preparatory activity is the subthreshold form of movement activity Churchland et al. 2010a

12. What is preparatory activity?Representational view • Hypothesis: preparatory activity is the subthreshold form of movement activity • Reality: preparatory & movement tuning are uncorrelated on average Churchland et al. 2010a

13. What is preparatory activity tuned for? Leave out one condition (direction), use linear regression to predict left-out preparatory firing rate from a set of “preferred directions”. PCA analysis: • Perimovement: activity of other neurons • Kinematic: position, velocity, acceleration • EMG: activity for multiple muscles dimensionality Best performance: from whole population dynamics. Churchland et al. 2010a

14. What is preparatory activity?Dynamical systems view • Hypothesis: preparatory activity brings population dynamical state to an initial value that produces correct motion with minimal reaction time. • Reduction in variability across different trials as states converge to muscle activation (FF) Churchland et al. 2010b

15. What is preparatory activity?Dynamical systems view • Hypothesis: preparatory activity brings population dynamical state to an initial value that produces correct motion with minimal reaction time. Churchland et al. 2010b

16. What is preparatory activity?Dynamical systems view • Hypothesis: preparatory activity brings population dynamical state to an initial value that produces correct motion with minimal reaction time. Churchland et al. 2010b

17. What is preparatory activity?Dynamical systems view • Hypothesis: preparatory activity brings population dynamical state to an initial value that produces correct motion with minimal reaction time. Churchland et al. 2010b

18. Convergence of trajectories • Reduction in variance comes from convergence of trajectories during motor preparation 10-D PCA Covariance ellipses Outlier (monkey hesitated) Churchland et al. 2010b

19. Convergence of trajectories • Reduction in variance comes from convergence of trajectories during motor preparation 10-D PCA Covariance ellipses Outlier (monkey hesitated) • Prediction: perturbing initial states near go cue should increase RT Churchland et al. 2010b

20. Preparation & response time • Use subthresholdmicrostimulation to perturb prepatory activity • No change in wave profile, change in RT only when perturbation occurs at go cue Churchland & Shenoy 2007a

21. Preparation & response time • Use subthresholdmicrostimulation to perturb prepatory activity • No change in wave profile, change in RT only when perturbation occurs at go cue • Change in RT due to preparatory state – less dramatic in M1, doesn’t exist in saccadic RT Churchland & Shenoy 2007a

22. Preparation & response time • corr(alpha,RT) ? Afshar et al 2011

23. Preparation & response time • corr(alpha,RT) < 0 • Farther along mean neural trajectory •  smaller RT Afshar et al 2011

24. PMd neural responses are bizarre • Tuning differs between preparatory & perimovement periods • Response are complex and multiphasic • Responses of different neurons are heterogeneous • Activity fluctuates longer than movement scale Churchland et al. 2010a

25. Low-dimensional activity is rotational • In low-dimensionality projections, trajectories rotate with phase set by preparatory state (captures ~28% variance) • However, the reaches were not overtly rhythmic • Brief sinusoidal oscillations form a basis set for more complex patterns Churchland et al. 2012

26. Neural population responses are rotational Churchland et al. 2012

27. Kinematic/EMG data are not Churchland et al. 2012

28. What is jPCA? • X = (n)x(ct) matrix • PCA to reduce to Xred = (k)x(ct) • Fit Xred’ = MXred, Xred’ = MskewXredusing linear regression • V1, V2 conjugate eigenvectors of Mskew • jPC1 = V1+V2 • jPC2 = j(V1-V2) • Project Xred onto jPC1, jPC2 Churchland et al. 2012

29. Conclusions • Dynamical systems approach gives insight into movement without making assumptions about single-neuron tuning. • “Preparation” funnels neural trajectories to achieve fast movement without minimal variation. • Preparatory state predicts both RT and trial-to-trial variability. • Rotational PMd firing rate dynamics form a basis for more complex muscular activity.