Alex Cayco Gajic
Journal Club 10/29/13

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

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

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

A few equations
• Representational view: r n
(t) = f n
(param
1
(t),param
2
(t),…)
• Dynamical systems perspective:
• Neural responses  muscle movement: m (t) = G[ r (t)]
But dimensionality of m << dimensionality of r , so G is probably not invertible
• Dynamical system for population activity:
τ r ’(t) = h( r (t)) + u (t)
• Dimensionality reduction techniques will be important to find robust, redundant activity patterns

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

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

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

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

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

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

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

What is preparatory activity tuned for?
Leave out one condition (direction), use linear regression to predict leftout 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

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

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

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

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

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

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

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

Preparation & response time
• Use subthreshold microstimulation 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

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

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

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

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

Neural population responses are rotational
Churchland et al. 2012

Churchland et al. 2012
Kinematic/EMG data are not

What is jPCA?
• X = (n)x(ct) matrix
• PCA to reduce to X red
• Fit X red
’ = MX red
, X red
= (k)x(ct)
’ = M skew
X red using linear regression
• V
1
, V
2 conjugate eigenvectors of
•
•
M skew jPC jPC
1
2
= V
1
+V
2
= j(V
• Project X
1 red
-V
2
) onto jPC
1
, jPC
2
Churchland et al. 2012

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.