Theoretical Considerations for the Analysis of Population Coding in Motor Cortex

1994 ◽  
Vol 6 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Terence D. Sanger
Keyword(s):  
Motor Cortex ◽  
Hand Movement ◽  
Personal Space ◽  
Population Coding ◽  
Movement Direction ◽  
Population Vector ◽  
Weak Support ◽  
Rectangular Coordinates ◽  
Cartesian Coordinate ◽  
Multiple Cells

Recent evidence of population coding in motor cortex has led some researchers to claim that certain variables such as hand direction or force may be coded within a Cartesian coordinate system with respect to extra personal space. These claims are based on the ability to predict the rectangular coordinates of hand movement direction using a “population vector” computed from multiple cells' firing rates. I show here that such a population vector can always be found given a very general set of assumptions. Therefore the existence of a population vector constitutes only weak support for the explicit use of a particular coordinate representation by motor cortex.

Changes in motor cortex activity during reaching movements with similar hand paths but different arm postures

1995 ◽  
Vol 73 (6) ◽  
pp. 2563-2567 ◽  
Author(s):  
S. H. Scott ◽  
J. F. Kalaska
Keyword(s):  
Motor Cortex ◽  
Sagittal Plane ◽  
Single Cells ◽  
Reaching Movements ◽  
Movement Direction ◽  
Tonic Activity ◽  
Population Vector ◽  
Significant Difference ◽  
Direction Of Movement ◽  
Kinematics And Kinetics

1. Neuronal activity was recorded in the motor cortex of a monkey that performed reaching movements with the use of two different arm postures. In the first posture (control), the monkey used its natural arm orientation, approximately in the sagittal plane. In the second posture (abducted), the monkey had to adduct its elbow nearly to shoulder level to grasp the handle. The path of the hand between targets was similar in both arm postures, but the joint kinematics and kinetics were different. 2. In both postures, the activity of single cells was often broadly tuned with movement direction and static arm posture over the targets. In a large proportion of cells, either the level of tonic activity, the directional tuning, or both, varied between the two postures during the movement and target hold periods. 3. For most directions of movement, there was a statistically significant difference in the direction of the population vector for the two arm postures. Furthermore, whereas the population vector tended to point in the direction of movement for the control posture, there was a poorer correspondence between the direction of movement and the population vector for the abducted posture. These observed changes are inconsistent with the notion that the motor cortex encodes purely hand trajectory in space.

Dynamical Organization of Directional Tuning in the Primate Premotor and Primary Motor Cortex

2003 ◽  
Vol 89 (2) ◽  
pp. 1136-1142 ◽  
Author(s):  
Yoram Ben-Shaul ◽  
Eran Stark ◽  
Itay Asher ◽  
Rotem Drori ◽  
Zoltan Nadasdy ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Hand Movement ◽  
Movement Direction ◽  
Cortical Surface ◽  
Preferred Direction ◽  
Preparation Period ◽  
Direction Of Movement ◽  
The Mean ◽  
Movement Parameters

Although previous studies have shown that activity of neurons in the motor cortex is related to various movement parameters, including the direction of movement, the spatial pattern by which these parameters are represented is still unresolved. The current work was designed to study the pattern of representation of the preferred direction (PD) of hand movement over the cortical surface. By studying pairwise PD differences, and by applying a novel implementation of the circular variance during preparation and movement periods in the context of a center-out task, we demonstrate a nonrandom distribution of PDs over the premotor and motor cortical surface of two monkeys. Our analysis shows that, whereas PDs of units recorded by nonadjacent electrodes are not more similar than expected by chance, PDs of units recorded by adjacent electrodes are. PDs of units recorded by a single electrode display the greatest similarity. Comparison of PD distributions during preparation and movement reveals that PDs of nearby units tend to be more similar during the preparation period. However, even for pairs of units recorded by a single electrode, the mean PD difference is typically large (45° and 75° during preparation and movement, respectively), so that a strictly modular representation of hand movement direction over the cortical surface is not supported by our data.

Earth's gravity field parameters determination by the space geodesy dynamical approach

2017 ◽  
Vol 919 (1) ◽  
pp. 7-12
Author(s):  
N.A Sorokin
Keyword(s):  
Coordinate System ◽  
Gravitational Potential ◽  
Coefficient Matrix ◽  
Measured Quantity ◽  
Second Derivative ◽  
Measured Variable ◽  
Second Derivatives ◽  
Rectangular Coordinates ◽  
Cartesian Coordinate ◽  
Derivatives Of

The method of the geopotential parameters determination with the use of the gradiometry data is considered. The second derivative of the gravitational potential in the correction equation on the rectangular coordinates x, y, z is used as a measured variable. For the calculated value of the measured quantity required for the formation of a free member of the correction equation, the the Cunningham polynomials were used. We give algorithms for computing the second derivatives of the Cunningham polynomials on rectangular coordinates x, y, z, which allow to calculate the second derivatives of the geopotential at the rectangular coordinates x, y, z.Then we convert derivatives obtained from the Cartesian coordinate system in the coordinate system of the gradiometer, which allow to calculate the free term of the correction equation. Afterwards the correction equation coefficients are calculated by differentiating the formula for calculating the second derivative of the gravitational potential on the rectangular coordinates x, y, z. The result is a coefficient matrix of the correction equations and corrections vector of the free members of equations for each component of the tensor of the geopotential. As the number of conditional equations is much more than the number of the specified parameters, we go to the drawing up of the system of normal equations, from which solutions we determine the required corrections to the harmonic coefficients.

Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex

2004 ◽  
Vol 98 (4-6) ◽  
pp. 498-506 ◽  
Author(s):  
Carsten Mehring ◽  
Martin Paul Nawrot ◽  
Simone Cardoso de Oliveira ◽  
Eilon Vaadia ◽  
Andreas Schulze-Bonhage ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Arm Movement ◽  
Movement Direction ◽  
Single Channels ◽  
Field Potentials ◽  
Human Motor Cortex ◽  
Human Motor

scholarly journals Motor Cortical Activity During Drawing Movements: Population Representation During Spiral Tracing

1999 ◽  
Vol 82 (5) ◽  
pp. 2693-2704 ◽  
Author(s):  
Daniel W. Moran ◽  
Andrew B. Schwartz
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Prediction Interval ◽  
Premotor Cortex ◽  
Cortical Activity ◽  
Simultaneous Action ◽  
Emg Activity ◽  
Radius Of Curvature ◽  
Time Interval ◽  
Population Vector

Monkeys traced spirals on a planar surface as unitary activity was recorded from either premotor or primary motor cortex. Using the population vector algorithm, the hand's trajectory could be accurately visualized with the cortical activity throughout the task. The time interval between this prediction and the corresponding movement varied linearly with the instantaneous radius of curvature; the prediction interval was longer when the path of the finger was more curved (smaller radius). The intervals in the premotor cortex fell into two groups, whereas those in the primary motor cortex formed a single group. This suggests that the change in prediction interval is a property of a single population in primary motor cortex, with the possibility that this outcome is due to the different properties generated by the simultaneous action of separate subpopulations in premotor cortex. Electromyographic (EMG) activity and joint kinematics were also measured in this task. These parameters varied harmonically throughout the task with many of the same characteristics as those of single cortical cells. Neither the lags between joint-angular velocities and hand velocity nor the lags between EMG and hand velocity could explain the changes in prediction interval between cortical activity and hand velocity. The simple spatial and temporal relationship between cortical activity and finger trajectory suggests that the figural aspects of this task are major components of cortical activity.

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