Biology subjects

Schweighofer, N.

Publications and source records attributed to Schweighofer, N..

25 records · Page 2Linked to original sources

bioRxiv2020

Reinforcement meta-learning optimizes visuomotor learning

Reinforcement learning enables the brain to learn optimal action selection, such as go or not go, by forming state-action and action-outcome associations. Does this mechanism also optimize the brains willingness to learn, such as learn or not learn? Learning to learn by rewards, i.e., reinforcement meta-learning, is a crucial mechanism for machines to develop flexibility in learning, which is also considered in the brain without empirical examinations. Here, we show that humans learn to learn or not learn to maximize rewards in visuomotor learning tasks. We also show that this regulation of learning is not a motivational bias but is a result of an instrumental, active process, which takes into account the learning-outcome structure. Our results thus demonstrate the existence of reinforcement meta-learning in the human brain. Because motor learning is a process of minimizing sensory errors, our findings uncover an essential mechanism of interaction between reward and error.

neuroscience↗

bioRxiv2019

When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance

Muscle synergies are usually identified via dimensionality reduction techniques, such that the identified synergies reconstruct the muscle activity to a level of accuracy defined heuristically, such as 90% of the variance explained. Here, we question the assumption that the residual muscle activity not explained by the synergies is due to noise. We hypothesize instead that the residual activity is structured and can therefore influence the execution of a motor task. Young healthy subjects performed an isometric reaching task in which surface electromyography of 10 arm muscles was mapped onto estimated two-dimensional forces used to control a cursor. Three to five synergies were extracted to account for 90% of the variance explained. We then altered the muscle-force mapping via \"hard\" and \"easy\" virtual surgeries. Whereas in both surgeries the forces associated with synergies spanned the same single dimension of the virtual environment, the muscle-force mapping was as close as possible to the initial mapping in the easy surgery and as far as possible in the hard surgery. This design therefore maximized potential differences in reaching errors attributable to the residual muscle activity. Results show that the easy surgery produced much smaller directional errors than the hard task. In addition, systematic estimations of the errors for easy and hard surgeries constructed with 1 to 10 synergies show that the errors differ significantly for up to 8 synergies, which account for 98% of the variance on average. Our study therefore indicates the need for cautious interpretations of results derived from synergy extraction techniques based on heuristics with lenient levels of accuracy.\n\nAuthor summaryThe muscle synergy hypothesis states that the central nervous system simplifies motor control by grouping muscles that share common functions into modules called muscle synergies. Current techniques use unsupervised dimensionality reduction algorithms to identify these synergies. However, these techniques rely on arbitrary criteria to determine the number of synergies, which is actually unknown. An example of such criteria is that the identified synergies must be able to reconstruct the measured muscle activity to at least a 90% level of accuracy. Thus, the residual muscle activity, the remaining 10% of the muscle activity, is often disregarded as noise. We show that residual muscle activity following muscle synergy identification has a large systematic effect on movements even when the number of synergies approaches the number of muscles. This suggests that current synergy extraction techniques may discard a component of muscle activity that is important for motor control. Therefore, current synergy extraction techniques must be updated to identify true physiological synergies.

neuroscience↗

bioRxiv2019

Electrical coupling controls dimensionality and chaotic firing of inferior olive neurons

One of the main challenges facing online neural learning systems with numerous modifiable parameters (or "degrees-of-freedom") such as the cerebellum, is how to avoid "overfitting". We previously proposed that the cerebellum controls the degree-of-freedoms during learning by gradually modulating the electric coupling strength between inferior olive neurons. Here, we develop a modeling technique to estimate effective coupling strengths between inferior olive neurons from in vivo recordings of Purkinje cell complex spike activity in three different coupling conditions. We show that high coupling strengths induce synchronous firing and decrease the dimensionality of inferior olive firing dynamics. In contrast, intermediate coupling strengths induce chaotic firing and increase the dimensionality of firing dynamics. Our results thus support the hypothesis that effective coupling controls the dimensionality of inferior olive firing, which may allow the olivocerebellar system to learn effectively from a small training sample set despite the low firing frequency of inferior olive neurons.

neuroscience↗

bioRxiv2018

High-definition transcranial direct current stimulation over the dorsal premotor cortex reliably modulates motor network excitability

The effectiveness of transcranial direct current stimulation (tDCS) placed over the motor hotspot (thought to represent the primary motor cortex (M1)) to modulate motor network excitability is highly variable. The premotor cortex--particularly the dorsal premotor cortex (PMd)--may be a promising alternative target to more effectively modulate motor excitability, as it influences motor control across multiple pathways, one independent of M1 and one with direct, modulating connections to M1. This double-blind, placebo-controlled study aimed to differentially excite motor and premotor regions using high-definition tDCS (HD-tDCS) with concurrent functional magnetic resonance imaging (fMRI). HD-tDCS applied over either the motor hotspot or the premotor cortex demonstrated high inter-individual variability in changes on cortical motor excitability. However, HD-tDCS over the premotor cortex led to a higher number of responders and greater changes in local fMRI-based complexity than HD-tDCS over the motor hotspot. Furthermore, an analysis of individual motor hotspot anatomical locations revealed that, in more than half of the participants, the motor hotspot is not located over anatomical M1 boundaries, despite using a canonical definition of the motor hotspot. This heterogeneity in stimulation site may contribute to the variability of tDCS results. Altogether, these findings provide new considerations to enhance tDCS reliability.

neuroscience↗

bioRxiv2018

Dosage Matters: A Randomized Controlled Trial of Rehabilitation Dose in the Chronic Phase after Stroke

Background and PurposeFor stroke rehabilitation, task-specific training in animal models and human rehabilitation trials is considered important to trigger inherent neuroplasticity, promote motor learning, and functional recovery. Little is known, however, about what constitutes an effective dosage of therapy.\n\nMethodsThis is a parallel group, four arm, single blind, phase I, randomized control trial of four dosages of upper extremity therapy delivered in an outpatient setting during the chronic phase after stroke. Participants were randomized into groups that varied in total dosage of therapy (i.e., 0, 15, 30, or 60 hours). Seven hundred and four participants were assessed for eligibility, 50 were eligible to enroll, 45 were randomized, 44 participated and 41 completed the study. Planned primary analyses used linear mixed effects regression to model baseline to post-intervention changes in the Motor Activity Log-Quality of Movement rating (MALQ) and the Wolf Motor Function Test (WMFT) time score as a function of therapy dosage. A series of hierarchical models were constructed using the MALQ and WMFT.\n\nResultsWe observed a significant dose response curve: the greater the dosage of training, the greater the change in MALQ, with the dose by week slope parameter of 0.0045 ({Delta}MAL/hour/week; p = 0.0011; 95% CI = [0.0019; 0.0071]). Over the 3 weeks of therapy, this corresponds to a gain of 0.81 in MALQ for the 60 hour dose.\n\nConclusionsFor mild-to-moderately impaired stroke survivors, the dosage of a patient-centered, task specific motor therapy was shown to systematically influence the gain in quality of arm use in the natural environment, but not functional capacity as measured in the laboratory. We highlight the importance of recovery outcomes that capture arm use vs. functional capacity.\n\nClinical Trial RegistrationURL: http://www.clinicaltrials.gov. Unique identifier: NCT 01749358

clinical trials↗

bioRxiv2018

To Overwrite or to Recall? Individual Differences in Motor Adaptation

The central nervous system predicts the consequences of motor commands by leaning multiple internal models of external perturbations and of the body. It is not well understood, however, how new internal models are created. Here, we propose a novel computational model of motor adaptation in which a stochastic Bayesian decision-making process determines whether i) a previously updated expert perturbation model is recalled and updated, ii) a novice model is selected and is updated into a new expert, or iii) the \"body\" model is updated. Results from computer simulations provide insights into various and contradictory experimental data on savings and error-clamp, and predicts qualitative individual differences in adaptation. We verified these predictions in a visuomotor adaptation experiment in which we varied the perturbation amplitudes as well as the amount of noise added to perturbation, and added \"trigger\" trials in the error-clamp condition. Single trigger trials led to largely qualitatively different behavior and can therefore be used to probe individual differences in memory updates between \"one-model\" and \"two-model\" learners. \"One-model\" learners continuously update the body model, showing no savings during re-adaptation to the perturbation, and gradual decay during error clamp. In contrast, \"two-model\" learners switch between an updated expert model and the body model, showing large savings during re-adaptation and stochastic lags during error clamp. Our results thus support the view that motor adaptation belongs to the general class of human learning according to which new memories are created when no existing memories can account for discontinuities in sensory data.\n\nShort summary/significanceWhen movements are followed by unexpected outcomes, such as following the introduction of a visuomotor or force field perturbation, or the sudden removal of such perturbations, it is unclear whether the central nervous system updates existing memories or creates new memories. Here, we propose a novel model of adaptation, and investigate, via simulation and behavioral experiments, how the amplitude and schedule of the perturbation, as well as the characteristics of the learner, lead to updates of existing memories or creation of new memories. Our results provide insights into a number of puzzling and contradictory experimental data on savings and error-clamp, as well as large qualitative individual differences in adaptation.

neuroscience↗

bioRxiv2018

What you predict, not what you do, updates memories in visuomotor adaptation

Sensory prediction errors are thought to update memories in motor 1 adaptation, but the role of performance errors is largely unknown. To dissociate these errors, we manipulated visual feedback during fast shooting movements under visuomotor rotation. Participants were instructed to strategically correct for performance errors by shooting to a neighboring target in one of four conditions: following the movement onset, the main target, the neighboring target, both targets, or none of the targets disappeared. Participants in all conditions experienced a drift away from the main target following the strategy. In conditions where the main target was shown, participants often tried to minimize performance errors caused by the drift by generating corrective movements. However, despite differences in performance during adaptation between conditions, memory decay in a delayed washout block was indistinguishable between conditions. Our results thus suggest that, in visuomotor adaptation, sensory predictions errors, but not performance errors, update the slow, temporally stable, component of motor memory.

neuroscience↗