Feedback adaptation to unpredictable force fields in 250ms

AbstractMotor learning and adaptation are important functions of the nervous system. Classical studies have characterized how humans adapt to changes in the environment during tasks such as reaching, and have documented improvements in behavior across movements. Yet little is known about how quickly the nervous system adapts to such disturbances. In particular, recent work has suggested that adaptation could be sufficiently fast to alter the control strategies of an ongoing movement. To further address the possibility that learning occurred within a single movement, we designed a series of human reaching experiments to extract in muscles recordings the latency of feedback adaptation. Our results confirmed that participants adapted their feedback responses to unanticipated force fields applied randomly. In addition, our analyses revealed that the feedback response was specifically and finely tuned to the ongoing perturbation not only across trials with the same force field, but also across different kinds of force fields. Finally, changes in muscle activity consistent with feedback adaptation occurred in about 250ms following reach onset. We submit this estimate as the latency of motor adaptation in the nervous system.

Download Full-text

Plastic Changes in Hand Proprioception Following Force-Field Motor Learning

Journal of Neurophysiology ◽

10.1152/jn.00543.2010 ◽

2010 ◽

Vol 104 (3) ◽

pp. 1213-1215 ◽

Cited By ~ 9

Author(s):

Daniel J. Goble ◽

Joaquin A. Anguera

Keyword(s):

Motor Learning ◽

Force Field ◽

Recent Work ◽

On the encoding capacity of human motor adaptation

Journal of Neurophysiology ◽

10.1152/jn.00593.2020 ◽

2021 ◽

Author(s):

Seung-Yeon Kim ◽

Jae-Woon Kwon ◽

Jin-Min Kim ◽

Frank Chong-Woo Park ◽

Sang-Hoon Yeo

Keyword(s):

Motor Learning ◽

Force Field ◽

Information Content ◽

Motor Adaptation ◽

Movement Speed ◽

Limiting Factor ◽

Information Encoding ◽

Motor Primitives ◽

Human Motor ◽

Human Motor Adaptation

Primitive-based models of motor learning suggest that adaptation occurs by tuning the responses of motor primitives. Based on this idea, we consider motor learning as an information encoding procedure, that is, a procedure of encoding a motor skill into primitives. The capacity of encoding is determined by the number of recruited primitives, which depends on how many primitives are "visited" by the movement, and this leads to a rather counter-intuitive prediction that faster movement, where a larger number of motor primitives are involved, allows learning more complicated motor skills. Here we provide a set of experimental results that support this hypothesis. First, we show that learning occurs only with movement, i.e., only with non-zero encoding capacity. When participants were asked to counteract a rotating force applied to a robotic handle, they were unable to do so when maintaining a static posture but were able to adapt when making small circular movements. Our second experiment further investigated how adaptation is affected by movement speed. When adapting to a simple (low-information-content) force field, fast (high-capacity) movement did not have an advantage over slow (low-capacity) movement. However, for a complex (high-information-content) force field, the fast movement showed a significant advantage over slow movement. Our final experiment confirmed that the observed benefit of high-speed movement is only weakly affected by mechanical factors. Taken together, our results suggest that the encoding capacity is a genuine limiting factor of human motor adaptation.

Download Full-text

A very fast time scale of human motor adaptation: within movement adjustments of internal representations during reaching

10.1101/269134 ◽

2018 ◽

Cited By ~ 2

Author(s):

F. Crevecoeur ◽

J.-L. Thonnard ◽

P. Lefèvre

Keyword(s):

Nervous System ◽

Feedback Control ◽

Motor Learning ◽

Force Field ◽

Time Scale ◽

Movement Control ◽

Fast Time ◽

After Effects ◽

Fast Time Scale ◽

Motor Commands

AbstractHumans and other animals adapt motor commands to predictable disturbances within tens of trials in laboratory conditions. A central question is how does the nervous system adapt to disturbances in natural conditions when exactly the same movements cannot be practiced several times. Because motor commands and sensory feedback together carry continuous information about limb dynamics, we hypothesized that the nervous system could adapt to unexpected disturbances online. We tested this hypothesis in two reaching experiments during which velocity-dependent force fields were randomly applied. We found that within-movement feedback corrections gradually improved, despite the fact that the perturbations were unexpected. Moreover, when participants were instructed to stop at a via-point, the application of a force field prior to the via-point induced mirror-image after-effects after the via-point, consistent with within-trial adaptation to the unexpected dynamics. These findings suggest a fast time-scale of motor learning, which complements feedback control and supports adaptation of an ongoing movement.Significance StatementAn important function of the nervous system is to adapt motor commands in anticipation of predictable disturbances, which supports motor learning when we move in novel environments such as force fields. Here we show that movement control when exposed to unpredictable disturbances exhibit similar traits: motor corrections become tuned to the force field, and they evoke after effects within an ongoing sequence of movements. We propose and discuss the framework of adaptive control to explain these results: a real-time learning algorithm, which complements feedback control in the presence of model errors. This candidate model potentially links movement control and trial-by-trial adaptation of motor commands.

Download Full-text

No effects of cerebellar transcranial direct current stimulation on force field and visuomotor reach adaptation in young and healthy subjects

Journal of Neurophysiology ◽

10.1152/jn.00352.2018 ◽

2019 ◽

Vol 121 (6) ◽

pp. 2112-2125 ◽

Cited By ~ 9

Author(s):

A. Mamlins ◽

T. Hulst ◽

O. Donchin ◽

D. Timmann ◽

J. Claassen

Keyword(s):

Motor Learning ◽

Force Field ◽

Direct Current ◽

Transcranial Direct Current Stimulation ◽

Healthy Subjects ◽

Motor Adaptation ◽

Visuomotor Rotation ◽

Direct Current Stimulation ◽

Cerebellar Tdcs ◽

Adaptation Task

Previous studies have shown that cerebellar transcranial direct current stimulation (tDCS) leads to faster adaptation of arm reaching movements to visuomotor rotation and force field perturbations in healthy subjects. The first aim of the present study was to confirm a stimulation-dependent effect on motor adaptation. Second, we investigated whether tDCS effects differ depending on onset, that is, before or at the beginning of the adaptation phase. A total of 120 healthy and right-handed subjects (60 women, mean age 23.2 ± SD 2.7 yr, range 18–31 yr) were tested. Subjects moved a cursor with a manipulandum to one of eight targets presented on a vertically orientated screen. Three baseline blocks were followed by one adaptation block and three washout blocks. Sixty subjects did a force field adaptation task (FF), and 60 subjects did a visuomotor adaptation task (VM). Equal numbers of subjects received anodal, cathodal, or sham cerebellar tDCS beginning either in the third baseline block or at the start of the adaptation block. In FF and VM, tDCS and the onset of tDCS did not show a significant effect on motor adaptation (all P values >0.05). We were unable to support previous findings of modulatory cerebellar tDCS effects in reaching adaptation tasks in healthy subjects. Prior to possible application in patients with cerebellar disease, future experiments are needed to determine which tDCS and task parameters lead to robust tDCS effects. NEW & NOTEWORTHY Transcranial direct current stimulation (tDCS) is a promising tool to improve motor learning. We investigated whether cerebellar tDCS improves motor learning in force field and visuomotor tasks in healthy subjects and what influence the onset of stimulation has. We did not find stimulation effects of tDCS or an effect of onset of stimulation. A reevaluation of cerebellar tDCS in healthy subjects and at the end of the clinical potential in cerebellar patients is demanded.

Download Full-text

Identifying the neural representation of fast and slow states in force field adaptation via fMRI

10.1101/582791 ◽

2019 ◽

Author(s):

Andria J. Farrens ◽

Fabrizio Sergi

Keyword(s):

Motor Learning ◽

Force Field ◽

Motor Adaptation ◽

Neural Representation ◽

Control Processes ◽

Behavioral Studies ◽

Motor Learning And Control ◽

Force Field Adaptation ◽

The Brain ◽

Slow Learning

AbstractNeurorehabilitation is centered on motor learning and control processes, however our understanding of how the brain learns to control movements is still limited. Motor adaptation is a rapid form of motor learning that is amenable to study in the laboratory setting. Behavioral studies of motor adaptation have coupled clever task design with computational modeling to study the control processes that underlie motor adaptation. These studies provide evidence of fast and slow learning states in the brain that combine to control neuromotor adaptation.Currently, the neural representation of these states remains unclear, especially for adaptation to changes in task dynamics, commonly studied using force fields imposed by a robotic device. Our group has developed the MR-Softwrist, a robot capable of executing dynamic adaptation tasks during functional magnetic resonance imaging (fMRI) that can be used to localize these networks in the brain.We simulated an fMRI experiment to determine if signal arising from a switching force field adaptation task can localize the neural representations of fast and slow learning states in the brain. Our results show that our task produces reliable behavioral estimates of fast and slow learning states, and distinctly measurable fMRI activations associated with each state under realistic levels of behavioral and measurement noise. Execution of this protocol with the MR-Softwrist will extend our knowledge of how the brain learns to control movement.

Download Full-text

Reward and punishment enhance motor adaptation in stroke

10.1101/106377 ◽

2017 ◽

Cited By ~ 1

Author(s):

Graziella Quattrocchi ◽

Richard Greenwood ◽

John C Rothwell ◽

Joseph M Galea ◽

Sven Bestmann

Keyword(s):

Motor Learning ◽

Force Field ◽

Motor Adaptation ◽

Similar Degree ◽

Stroke Patients ◽

Neutral Group ◽

Rehabilitation Outcomes ◽

First Time ◽

Adaptation Phase ◽

Reward And Punishment

ABSTRACTThe effects of motor learning, such as motor adaptation, in stroke rehabilitation are often transient, thus mandating approaches that enhance the amount of learning and retention. Previously, we showed in young individuals that reward-and punishment-feedback have dissociable effects on motor adaptation, with punishment improving adaptation and reward enhancing retention. If these findings were able to generalise to stroke patients, they would provide a way to optimize motor learning in these patients. Therefore, we tested this in 45 chronic stroke patients allocated in three groups. Patients performed reaching movements with their paretic arm with a robotic manipulandum. After training (day 1), day 2 involved adapting to a novel force-field. During this adaptation phase, patients received performance-based feedback according to the group they were allocated: reward, punishment or no feedback (neutral). On day 3, patients readapted to the force-field but all groups now received neutral feedback. All patients adapted, with reward and punishment groups displaying greater adaptation and readaptation than the neutral group, irrespective of demographic, cognitive or functional differences. Remarkably, the reward and punishment groups adapted to similar degree as healthy controls. Finally, the reward group showed greater retention. This study provides, for the first time, evidence that reward and punishment can enhance motor adaptation in stroke patients. Further research on reinforcement-based motor learning regimes is warranted to translate these promising results into clinical practice and improve motor rehabilitation outcomes in stroke patients.

Download Full-text

Hydration Free Energies of Organic Molecules in the FreeSolv Database Calculated with Polarized Atom In Molecules Atomic Charges and the GAFF Force Field.

10.26434/chemrxiv.6015611.v1 ◽

2018 ◽

Author(s):

Maximiliano Riquelme ◽

Alejandro Lara ◽

David L. Mobley ◽

Toon Vestraelen ◽

Adelio R Matamala ◽

...

Keyword(s):

Force Field ◽

Functional Groups ◽

Electrostatic Interactions ◽

Organic Molecules ◽

Force Fields ◽

Chemical Elements ◽

Atomic Charges ◽

Free Energies ◽

Molecular Systems ◽

Solvent Model

<div>Computer simulations of bio-molecular systems often use force fields, which are combinations of simple empirical atom-based functions to describe the molecular interactions. Even though polarizable force fields give a more detailed description of intermolecular interactions, nonpolarizable force fields, developed several decades ago, are often still preferred because of their reduced computation cost. Electrostatic interactions play a major role in bio-molecular systems and are therein described by atomic point charges.</div><div>In this work, we address the performance of different atomic charges to reproduce experimental hydration free energies in the FreeSolv database in combination with the GAFF force field. Atomic charges were calculated by two atoms-in-molecules approaches, Hirshfeld-I and Minimal Basis Iterative Stockholder (MBIS). To account for polarization effects, the charges were derived from the solute's electron density computed with an implicit solvent model and the energy required to polarize the solute was added to the free energy cycle. The calculated hydration free energies were analyzed with an error model, revealing systematic errors associated with specific functional groups or chemical elements. The best agreement with the experimental data is observed for the MBIS atomic charge method, including the solvent polarization, with a root mean square error of 2.0 kcal mol<sup>-1</sup> for the 613 organic molecules studied. The largest deviation was observed for phosphor-containing molecules and the molecules with amide, ester and amine functional groups.</div>

Download Full-text

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

10.26434/chemrxiv.7247045.v2 ◽

2019 ◽

Cited By ~ 1

Author(s):

Joshua Horton ◽

Alice Allen ◽

Leela Dodda ◽

Daniel Cole

Keyword(s):

Quantum Mechanics ◽

Force Field ◽

Organic Molecules ◽

Force Fields ◽

Liquid Density ◽

Small Organic Molecules ◽

Automated Generation ◽

Energy Of Hydration ◽

Novel Method ◽

Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

Download Full-text

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

10.26434/chemrxiv.7247045 ◽

2019 ◽

Cited By ~ 1

Author(s):

Joshua Horton ◽

Alice Allen ◽

Leela Dodda ◽

Daniel Cole

Keyword(s):

Quantum Mechanics ◽

Force Field ◽

Organic Molecules ◽

Force Fields ◽

Liquid Density ◽

Small Organic Molecules ◽

Automated Generation ◽

Energy Of Hydration ◽

Novel Method ◽

Force Field Parameters

Download Full-text

A Transferable, Polarisable Force Field for Ionic Liquids

10.26434/chemrxiv.8845526.v1 ◽

2019 ◽

Author(s):

Kateryna Goloviznina ◽

José N. Canongia Lopes ◽

Margarida Costa Gomes ◽

Agilio Padua

Keyword(s):

Ionic Liquids ◽

Force Field ◽

Force Fields ◽

Dipole Moments ◽

Van Der Waals Interactions ◽

First Principles Calculations ◽

Ion Diffusion ◽

Fixed Integer ◽

Molecular Compounds ◽

Induced Dipoles

A general, transferable polarisable force field for molecular simulation of ionic liquids and their mixtures with molecular compounds is developed. This polarisable model is derived from the widely used CL\&P fixed-charge force field that describes most families of ionic liquids, in a form compatible with OPLS-AA, one of the major force fields for organic compounds. Models for ionic liquids with fixed, integer ionic charges lead to pathologically slow dynamics, a problem that is corrected when polarisation effects are included explicitly. In the model proposed here, Drude induced dipoles are used with parameters determined from atomic polarisabilities. The CL\&P force field is modified upon inclusion of the Drude dipoles, to avoid double-counting of polarisation effects. This modification is based on first-principles calculations of the dispersion and induction contributions to the van der Waals interactions, using symmetry-adapted perturbation theory (SAPT) for a set of dimers composed of positive, negative and neutral fragments representative of a wide variety of ionic liquids. The fragment approach provides transferability, allowing the representation of a multitude of cation and anion families, including different functional groups, without need to re-parametrise. Because SAPT calculations are expensive an alternative predictive scheme was devised, requiring only molecular properties with a clear physical meaning, namely dipole moments and atomic polarisabilities. The new polarisable force field, CL\&Pol, describes a broad set set of ionic liquids and their mixtures with molecular compounds, and is validated by comparisons with experimental data on density, ion diffusion coefficients and viscosity. The approaches proposed here can also be applied to the conversion of other fixed-charged force fields into polarisable versions.<br>

Download Full-text