Limits...
Leg mechanics contribute to establishing swing phase trajectories during memory-guided stepping movements in walking cats: a computational analysis.

Pearson KG, Arbabzada N, Gramlich R, Shinya M - Front Comput Neurosci (2015)

Bottom Line: However, an additional contribution of neuronal motor commands was indicated by the fact that the simulated slopes of paw trajectories were significantly less than the observed slopes.Previous studies have shown that a shift in paw position prior to stepping over a barrier changes the paw trajectory to be appropriate for the new paw position.Our data indicate that both mechanical and neuronal factors contribute to this updating process, and that any shift in leg position during the delay period modifies the working memory of barrier location.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, University of Alberta Edmonton, AB, Canada.

ABSTRACT
When quadrupeds stop walking after stepping over a barrier with their forelegs, the memory of barrier height and location is retained for many minutes. This memory is subsequently used to guide hind leg movements over the barrier when walking is resumed. The upslope of the initial trajectory of hind leg paw movements is strongly dependent on the initial location of the paw relative to the barrier. In this study, we have attempted to determine whether mechanical factors contribute significantly in establishing the slope of the paw trajectories by creating a four-link biomechanical model of a cat hind leg and driving this model with a variety of joint-torque profiles, including average torques for a range of initial paw positions relative to the barrier. Torque profiles for individual steps were determined by an inverse dynamic analysis of leg movements in three normal cats. Our study demonstrates that limb mechanics can contribute to establishing the dependency of trajectory slope on the initial position of the paw relative to the barrier. However, an additional contribution of neuronal motor commands was indicated by the fact that the simulated slopes of paw trajectories were significantly less than the observed slopes. A neuronal contribution to the modification of paw trajectories was also revealed by our observations that both the magnitudes of knee flexor muscle EMG bursts and the initial knee flexion torques depended on initial paw position. Previous studies have shown that a shift in paw position prior to stepping over a barrier changes the paw trajectory to be appropriate for the new paw position. Our data indicate that both mechanical and neuronal factors contribute to this updating process, and that any shift in leg position during the delay period modifies the working memory of barrier location.

No MeSH data available.


Verification of accuracy of numerical integration of the forward mechanical model. (A) The kinematics of the hind leg of a cat stepping over a barrier (actual kinematics) were used in an inverse dynamic analysis to first calculate the torque profiles at the four joints. These torque profiles were then used as inputs to the forward model to yield a simulated leg movement (model kinematics). Note the high degree of similarity of the stick figures for the actual and simulated leg movements. (B) Plot of the slopes of the actual and simulated toe trajectories showing a very close correspondence.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4585078&req=5

Figure 2: Verification of accuracy of numerical integration of the forward mechanical model. (A) The kinematics of the hind leg of a cat stepping over a barrier (actual kinematics) were used in an inverse dynamic analysis to first calculate the torque profiles at the four joints. These torque profiles were then used as inputs to the forward model to yield a simulated leg movement (model kinematics). Note the high degree of similarity of the stick figures for the actual and simulated leg movements. (B) Plot of the slopes of the actual and simulated toe trajectories showing a very close correspondence.

Mentions: To demonstrate that the numerical integration was accurate, and that the forward simulation produced appropriate leg movements, we initially drove the forward model with the profiles of joint torques derived from the inverse dynamic analysis of single trials. Figure 2A shows sets of stick diagrams of leg geometry for a single stepping trial measured from a stepping animal (left) and generated by the forward model (right). During the early part of the swing phase the leg movements produced by the forward model closely matched the leg movements observed in the behaving animals. This was illustrated by the close correspondence of slopes of the toe trajectories in the simulation and in the behaving animal (Figure 2B). Small difference in geometry occurred near the end of the step (Figure 2A), but these occur well beyond the times of the events we were measuring (early in the swing phase). Similar matches were obtained for all trials we examined, regardless of the initial geometry of the leg. Thus, we are confident that the method of numerical integration (4th order Runge Kutta) was appropriate for our study.


Leg mechanics contribute to establishing swing phase trajectories during memory-guided stepping movements in walking cats: a computational analysis.

Pearson KG, Arbabzada N, Gramlich R, Shinya M - Front Comput Neurosci (2015)

Verification of accuracy of numerical integration of the forward mechanical model. (A) The kinematics of the hind leg of a cat stepping over a barrier (actual kinematics) were used in an inverse dynamic analysis to first calculate the torque profiles at the four joints. These torque profiles were then used as inputs to the forward model to yield a simulated leg movement (model kinematics). Note the high degree of similarity of the stick figures for the actual and simulated leg movements. (B) Plot of the slopes of the actual and simulated toe trajectories showing a very close correspondence.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585078&req=5

Figure 2: Verification of accuracy of numerical integration of the forward mechanical model. (A) The kinematics of the hind leg of a cat stepping over a barrier (actual kinematics) were used in an inverse dynamic analysis to first calculate the torque profiles at the four joints. These torque profiles were then used as inputs to the forward model to yield a simulated leg movement (model kinematics). Note the high degree of similarity of the stick figures for the actual and simulated leg movements. (B) Plot of the slopes of the actual and simulated toe trajectories showing a very close correspondence.
Mentions: To demonstrate that the numerical integration was accurate, and that the forward simulation produced appropriate leg movements, we initially drove the forward model with the profiles of joint torques derived from the inverse dynamic analysis of single trials. Figure 2A shows sets of stick diagrams of leg geometry for a single stepping trial measured from a stepping animal (left) and generated by the forward model (right). During the early part of the swing phase the leg movements produced by the forward model closely matched the leg movements observed in the behaving animals. This was illustrated by the close correspondence of slopes of the toe trajectories in the simulation and in the behaving animal (Figure 2B). Small difference in geometry occurred near the end of the step (Figure 2A), but these occur well beyond the times of the events we were measuring (early in the swing phase). Similar matches were obtained for all trials we examined, regardless of the initial geometry of the leg. Thus, we are confident that the method of numerical integration (4th order Runge Kutta) was appropriate for our study.

Bottom Line: However, an additional contribution of neuronal motor commands was indicated by the fact that the simulated slopes of paw trajectories were significantly less than the observed slopes.Previous studies have shown that a shift in paw position prior to stepping over a barrier changes the paw trajectory to be appropriate for the new paw position.Our data indicate that both mechanical and neuronal factors contribute to this updating process, and that any shift in leg position during the delay period modifies the working memory of barrier location.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, University of Alberta Edmonton, AB, Canada.

ABSTRACT
When quadrupeds stop walking after stepping over a barrier with their forelegs, the memory of barrier height and location is retained for many minutes. This memory is subsequently used to guide hind leg movements over the barrier when walking is resumed. The upslope of the initial trajectory of hind leg paw movements is strongly dependent on the initial location of the paw relative to the barrier. In this study, we have attempted to determine whether mechanical factors contribute significantly in establishing the slope of the paw trajectories by creating a four-link biomechanical model of a cat hind leg and driving this model with a variety of joint-torque profiles, including average torques for a range of initial paw positions relative to the barrier. Torque profiles for individual steps were determined by an inverse dynamic analysis of leg movements in three normal cats. Our study demonstrates that limb mechanics can contribute to establishing the dependency of trajectory slope on the initial position of the paw relative to the barrier. However, an additional contribution of neuronal motor commands was indicated by the fact that the simulated slopes of paw trajectories were significantly less than the observed slopes. A neuronal contribution to the modification of paw trajectories was also revealed by our observations that both the magnitudes of knee flexor muscle EMG bursts and the initial knee flexion torques depended on initial paw position. Previous studies have shown that a shift in paw position prior to stepping over a barrier changes the paw trajectory to be appropriate for the new paw position. Our data indicate that both mechanical and neuronal factors contribute to this updating process, and that any shift in leg position during the delay period modifies the working memory of barrier location.

No MeSH data available.