scholarly journals Performance Limitations in Sensorimotor Control: Tradeoffs between Neural Computation and Accuracy in Tracking Fast Movements

2018 ◽  
Author(s):  
Shreya Saxena ◽  
Sridevi V. Sarma ◽  
Munther Dahleh
Keyword(s):  
Moving Objects ◽  
Primary Motor Cortex ◽  
Linear Models ◽  
Sensorimotor Control ◽  
Fundamental Limitations ◽  
Muscle Dynamics ◽  
Scs Model ◽  
Nonlinear Control Theory ◽  
Performance Limitations ◽  
Overshoot And Undershoot

The ability to move fast and accurately track moving objects is fundamentally constrained by the biophysics of neurons and dynamics of the muscles involved. Yet, the corresponding tradeoffs between these factors and tracking motor commands have not been rigorously quantified. We use feedback control principles to quantify performance limitations of the sensorimotor control system (SCS) to track fast periodic movements. We show that (i) linear models of the SCS fail to predict known undesirable phenomena, including skipped cycles, overshoot and undershoot, produced when tracking signals in the “fast regime”, while non-linear pulsatile control models can predict such undesirable phenomena, and (ii) tools from nonlinear control theory allow us to characterize fundamental limitations in this fast regime. Using a validated and tractable nonlinear model of the SCS, we derive an analytical upper bound on frequencies that the SCS model can reliably track before producing such undesirable phenomena as a function of the neurons’ biophysical constraints and muscle dynamics. The performance limitations derived here have important implications in sensorimotor control. For example, if primary motor cortex is compromised due to disease or damage, the theory suggests ways to manipulate muscle dynamics by adding the necessary compensatory forces using an assistive neuroprosthetic device to restore motor performance, and more importantly fast and agile movements. Just how one should compensate can be informed by our SCS model and the theory developed here.

scholarly journals Performance Limitations in Sensorimotor Control: Trade-Offs Between Neural Computation and Accuracy in Tracking Fast Movements

2020 ◽  
Vol 32 (5) ◽  
pp. 865-886
Author(s):  
Shreya Saxena ◽  
Sridevi V. Sarma ◽  
Munther Dahleh
Keyword(s):  
Moving Objects ◽  
Primary Motor Cortex ◽  
Linear Models ◽  
Sensorimotor Control ◽  
Trade Offs ◽  
Muscle Dynamics ◽  
Scs Model ◽  
Nonlinear Control Theory ◽  
Performance Limitations ◽  
Overshoot And Undershoot

The ability to move fast and accurately track moving objects is fundamentally constrained by the biophysics of neurons and dynamics of the muscles involved. Yet the corresponding trade-offs between these factors and tracking motor commands have not been rigorously quantified. We use feedback control principles to quantify performance limitations of the sensorimotor control system (SCS) to track fast periodic movements. We show that (1) linear models of the SCS fail to predict known undesirable phenomena, including skipped cycles, overshoot and undershoot, produced when tracking signals in the “fast regime,” while nonlinear pulsatile control models can predict such undesirable phenomena, and (2) tools from nonlinear control theory allow us to characterize fundamental limitations in this fast regime. Using a validated and tractable nonlinear model of the SCS, we derive an analytical upper bound on frequencies that the SCS model can reliably track before producing such undesirable phenomena as a function of the neurons' biophysical constraints and muscle dynamics. The performance limitations derived here have important implications in sensorimotor control. For example, if the primary motor cortex is compromised due to disease or damage, the theory suggests ways to manipulate muscle dynamics by adding the necessary compensatory forces using an assistive neuroprosthetic device to restore motor performance and, more important, fast and agile movements. Just how one should compensate can be informed by our SCS model and the theory developed here.

Movement-Related Neuronal Activity Reflecting the Transformation of Coordinates in the Ventral Premotor Cortex of Monkeys

2002 ◽  
Vol 88 (6) ◽  
pp. 3118-3132 ◽  
Author(s):  
Kiyoshi Kurata ◽  
Eiji Hoshi
Keyword(s):  
Neuronal Activity ◽  
Primary Motor Cortex ◽  
Linear Models ◽  
Target Location ◽  
Movement Time ◽  
Premotor Cortex ◽  
Visual Space ◽  
Behavioral Task ◽  
Movement Onset ◽  
Ventral Premotor Cortex

We examined how the transformation of coordinates from visual to motor space is reflected by neuronal activity in the ventral premotor cortex (PMv) of monkeys. Three monkeys were trained to reach with their right hand for a target that appeared on a screen. While performing the task, the monkeys wore prisms that shifted the image of the target 10°, left or right, or wore no prisms, for a block of 200 trials. The nine targets were located in the same positions in visual space regardless of whether the prisms were present. Wearing the prisms required the monkeys to initiate a movement in a direction that was different from the apparent target location. Thus using the prisms, we could dissociate visual space from motor space. While the monkey performed the behavioral task, we recorded neuronal activity in the left PMv and primary motor cortex (MI), and various kinds of task-related neuronal activity were found in the motor areas. These included neurons that changed their activity during a reaction time (RT) period (the period between target presentation and movement onset), which were called “movement-related neurons” and selected for analysis. In these neurons, activity during a movement time (MT) period was also compared. Using general linear models for our statistical analysis, the neurons were then classified into four types: those whose activity was consistently dependent on location of targets in the visual coordinates regardless of whether the prisms were present or absent (V type); those that were consistently dependent on target location in the motor coordinates only; those that had different activity for both of the motor and visual coordinates; and those that had nondifferential activity for the two types of coordinates. The proportion of the four types of the neurons differed significantly between the PMv and MI. Most remarkably, neurons with V-type activity were almost exclusively recorded in the PMv and were almost exclusively found during the RT period. Such activity was never observed in an electromyogram of the working forelimb. Based on these observations, we postulate that the V and other types may represent the various intermediate stages of the transformation of coordinates and that the PMv plays a crucial role in transforming coordinates from visual to motor space.

scholarly journals The primary motor cortex prospectively computes the spinal reflex

2020 ◽  
Author(s):  
Tatsuya Umeda ◽  
Tadashi Isa ◽  
Yukio Nishimura
Keyword(s):  
Motor Cortex ◽  
Muscle Activity ◽  
Muscle Activation ◽  
Primary Motor Cortex ◽  
Linear Models ◽  
Movement Control ◽  
Spinal Reflex ◽  
Afferent Activity ◽  
Precise Movement ◽  
Behaving Monkeys

AbstractThe spinal reflex transforms sensory signals to generate muscle activity. However, it is unknown how the motor cortex (MCx) takes the spinal reflex into account when performing voluntary limb movements. We simultaneously recorded the activity of the MCx, afferent neurons, and forelimb muscles in behaving monkeys. We decomposed muscle activity into subcomponents explained by the MCx or afferent activity using linear models. Long preceding activity in the MCx, which is responsible for subsequent afferent activity, had the same spatiotemporal contribution to muscle activity as afferent activity, indicating that the MCx drives muscle activity not only by direct descending activation but also by trans-afferent descending activation. Therefore, the MCx implements internal models that prospectively estimate muscle activation via the spinal reflex for precise movement control.

scholarly journals A modular neural network model of grasp movement generation

2019 ◽  
Author(s):  
Jonathan A. Michaels ◽  
Stefan Schaffelhofer ◽  
Andres Agudelo-Toro ◽  
Hansjörg Scherberger
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Primary Motor Cortex ◽  
Premotor Cortex ◽  
Neural Data ◽  
Network Training ◽  
Muscle Dynamics ◽  
Lesion Studies ◽  
Object Feature ◽  
Hand Area

SummaryOne of the primary ways we interact with the world is using our hands. In macaques, the circuit spanning the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. We hypothesized that a recurrent neural network mimicking the multi-area structure of the anatomical circuit and using visual features to generate the required muscle dynamics to grasp objects would explain the neural and computational basis of the grasping circuit. Modular networks with object feature input and sparse inter-module connectivity outperformed other models at explaining neural data and the inter-area relationships present in the biological circuit, despite the absence of neural data during network training. Network dynamics were governed by simple rules, and targeted lesioning of modules produced deficits similar to those observed in lesion studies, providing a potential explanation for how grasping movements are generated.

scholarly journals A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping

2020 ◽  
Vol 117 (50) ◽  
pp. 32124-32135 ◽  
Author(s):  
Jonathan A. Michaels ◽  
Stefan Schaffelhofer ◽  
Andres Agudelo-Toro ◽  
Hansjörg Scherberger
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Primary Motor Cortex ◽  
Initial Conditions ◽  
Premotor Cortex ◽  
Brain Regions ◽  
Network Activity ◽  
Visually Guided ◽  
Neural Data ◽  
Muscle Dynamics

One of the primary ways we interact with the world is using our hands. In macaques, the circuit spanning the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. However, no comprehensive model exists that links all steps of processing from vision to action. We hypothesized that a recurrent neural network mimicking the modular structure of the anatomical circuit and trained to use visual features of objects to generate the required muscle dynamics used by primates to grasp objects would give insight into the computations of the grasping circuit. Internal activity of modular networks trained with these constraints strongly resembled neural activity recorded from the grasping circuit during grasping and paralleled the similarities between brain regions. Network activity during the different phases of the task could be explained by linear dynamics for maintaining a distributed movement plan across the network in the absence of visual stimulus and then generating the required muscle kinematics based on these initial conditions in a module-specific way. These modular models also outperformed alternative models at explaining neural data, despite the absence of neural data during training, suggesting that the inputs, outputs, and architectural constraints imposed were sufficient for recapitulating processing in the grasping circuit. Finally, targeted lesioning of modules produced deficits similar to those observed in lesion studies of the grasping circuit, providing a potential model for how brain regions may coordinate during the visually guided grasping of objects.

A Note on Effects of Digital Approximation in Linear Models

1974 ◽  
Vol 3 (9) ◽  
pp. 893-897
Author(s):  
Gerald McWilliams ◽  
James Poirot†
Keyword(s):  
Linear Models

Effects of Momentary Affect on Satisfaction Judgments

2020 ◽  
Vol 41 (2) ◽  
pp. 61-67
Author(s):  
Marko Tončić ◽  
Petra Anić
Keyword(s):  
Linear Models ◽  
Time Frame ◽  
Hierarchical Linear Models ◽  
Curvilinear Relationship ◽  
Positive And Negative Affect ◽  
Transient Events ◽  
Time Frames ◽  
Events Data ◽  
Level Satisfaction ◽  
Source Of Information

Abstract. This study aims to examine the effect of affect on satisfaction, both at the between- and the within-person level for momentary assessments. Affect is regarded as an important source of information for life satisfaction judgments. This affective effect on satisfaction is well established at the dispositional level, while at the within-person level it is heavily under-researched. This is true especially for momentary assessments. In this experience sampling study both mood and satisfaction scales were administered five times a day for 7 days via hand-held devices ( N = 74 with 2,122 assessments). Several hierarchical linear models were fitted to the data. Even though the amount of between-person variance was relatively low, both positive and negative affect had substantial effects on momentary satisfaction on the between- and the within-person level as well. The within-person effects of affect on satisfaction appear to be more pronounced than the between-person ones. At the momentary level, the amount of between-person variance is lower than in studies with longer time-frames. The affect-related effects on satisfaction possibly have a curvilinear relationship with the time-frame used, increasing in intensity up to a point and then decreasing again. Such a relationship suggests that, at the momentary level, satisfaction might behave in a more stochastic manner, allowing for transient events/data which are not necessarily affect-related to affect it.

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