scholarly journals State dependence of adaptation of force output following movement observation

2013 ◽  
Vol 110 (5) ◽  
pp. 1246-1256 ◽  
Author(s):  
Paul A. Wanda ◽  
Gang Li ◽  
Kurt A. Thoroughman
Keyword(s):  
Action Observation ◽  
Motor Task ◽  
State Dependence ◽  
Lateral Force ◽  
Neural Representation ◽  
The Novel ◽  
Learning Effects ◽  
Force Output ◽  
State Dependent ◽  
The Brain

Humans readily learn to move through direct physical practice and by watching the movements of others. Some researchers have proposed that action observation can inform subsequent changes in control through the acquisition of a neural representation of the novel dynamics, but to date learning following observation has been described by kinematic metrics. Here we designed an experiment to consider the specificity of adaptation to novel dynamic perturbations at the level of force generation. We measured changes in temporal patterns of force output following either the performance or observation of movements perturbed by either position- or velocity-dependent dynamic environments to 1) establish whether previously described observational motor learning effects were attributable to changes in predictive limb control and 2) determine whether such adaptation reflected a learned dependence on limb states appropriate to the haptic environment. We found that subjects who observed perturbed movements produced significant compensatory changes in their lateral force output, despite never directly experiencing force perturbations firsthand while performing the motor task. The time series of observers' adapted force outputs suggested that the state dependence of observed dynamics shapes adaptation. We conclude that the brain can transform observation of kinematics into state-dependent adaptation of reach dynamics.

scholarly journals A Neural Representation of Sequential States Within an Instructed Task

2010 ◽  
Vol 104 (5) ◽  
pp. 2831-2849 ◽  
Author(s):  
Michael Campos ◽  
Boris Breznen ◽  
Richard A. Andersen
Keyword(s):  
Internal Representation ◽  
Neural Representation ◽  
Neural Basis ◽  
Lateral Intraparietal Area ◽  
State Dependent ◽  
Sensorimotor Transformations ◽  
Saccade Task ◽  
Anticipatory Activity ◽  
Supplementary Eye Fields ◽  
The Brain

In the study of the neural basis of sensorimotor transformations, it has become clear that the brain does not always wait to sense external events and afterward select the appropriate responses. If there are predictable regularities in the environment, the brain begins to anticipate the timing of instructional cues and the signals to execute a response, revealing an internal representation of the sequential behavioral states of the task being performed. To investigate neural mechanisms that could represent the sequential states of a task, we recorded neural activity from two oculomotor structures implicated in behavioral timing—the supplementary eye fields (SEF) and the lateral intraparietal area (LIP)—while rhesus monkeys performed a memory-guided saccade task. The neurons of the SEF were found to collectively encode the progression of the task with individual neurons predicting and/or detecting states or transitions between states. LIP neurons, while also encoding information about the current temporal interval, were limited with respect to SEF neurons in two ways. First, LIP neurons tended to be active when the monkey was planning a saccade but not in the precue or intertrial intervals, whereas SEF neurons tended to have activity modulation in all intervals. Second, the LIP neurons were more likely to be spatially tuned than SEF neurons. SEF neurons also show anticipatory activity. The state-selective and anticipatory responses of SEF neurons support two complementary models of behavioral timing, state dependent and accumulator models, and suggest that each model describes a contribution SEF makes to timing at different temporal resolutions.

The Neural Representation of Events Is Dominated by Elements that Are Most Reliably Present

2021 ◽  
pp. 1-15
Author(s):  
Konstantinos Bromis ◽  
Petar P. Raykov ◽  
Leah Wickens ◽  
Warrick Roseboom ◽  
Chris M. Bird
Keyword(s):  
Episodic Memory ◽  
Specific Activity ◽  
Activity Patterns ◽  
Neural Representation ◽  
The Novel ◽  
Experimental Paradigm ◽  
Multiple Events ◽  
The People ◽  
The Brain ◽  
Posterior Midline

Abstract An episodic memory is specific to an event that occurred at a particular time and place. However, the elements that comprise the event—the location, the people present, and their actions and goals—might be shared with numerous other similar events. Does the brain preferentially represent certain elements of a remembered event? If so, which elements dominate its neural representation: those that are shared across similar events, or the novel elements that define a specific event? We addressed these questions by using a novel experimental paradigm combined with fMRI. Multiple events were created involving conversations between two individuals using the format of a television chat show. Chat show “hosts” occurred repeatedly across multiple events, whereas the “guests” were unique to only one event. Before learning the conversations, participants were scanned while viewing images or names of the (famous) individuals to be used in the study to obtain person-specific activity patterns. After learning all the conversations over a week, participants were scanned for a second time while they recalled each event multiple times. We found that during recall, person-specific activity patterns within the posterior midline network were reinstated for the hosts of the shows but not the guests, and that reinstatement of the hosts was significantly stronger than the reinstatement of the guests. These findings demonstrate that it is the more generic, familiar, and predictable elements of an event that dominate its neural representation compared with the more idiosyncratic, event-defining, elements.

State-dependent signalling in queueing networks

1994 ◽  
Vol 26 (02) ◽  
pp. 436-455 ◽  
Author(s):  
W. Henderson ◽  
B. S. Northcote ◽  
P. G. Taylor
Keyword(s):  
Queueing Networks ◽  
State Dependence ◽  
Product Form ◽  
Batch Size ◽  
Single Server ◽  
Negative Customers ◽  
Affect Control ◽  
State Dependent ◽  
Special Cases ◽  
Equilibrium Distributions

It has recently been shown that networks of queues with state-dependent movement of negative customers, and with state-independent triggering of customer movement have product-form equilibrium distributions. Triggers and negative customers are entities which, when arriving to a queue, force a single customer to be routed through the network or leave the network respectively. They are ‘signals' which affect/control network behaviour. The provision of state-dependent intensities introduces queues other than single-server queues into the network. This paper considers networks with state-dependent intensities in which signals can be either a trigger or a batch of negative customers (the batch size being determined by an arbitrary probability distribution). It is shown that such networks still have a product-form equilibrium distribution. Natural methods for state space truncation and for the inclusion of multiple customer types in the network can be viewed as special cases of this state dependence. A further generalisation allows for the possibility of signals building up at nodes.

scholarly journals A copula-based multivariate hidden Markov model for modelling momentum in football

2021 ◽  
Author(s):  
Marius Ötting ◽  
Roland Langrock ◽  
Antonello Maruotti
Keyword(s):  
Markov Model ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
State Dependence ◽  
Change Points ◽  
Sports Fans ◽  
Modelling Framework ◽  
State Dependent ◽  
Different Levels

AbstractWe investigate the potential occurrence of change points—commonly referred to as “momentum shifts”—in the dynamics of football matches. For that purpose, we model minute-by-minute in-game statistics of Bundesliga matches using hidden Markov models (HMMs). To allow for within-state dependence of the variables, we formulate multivariate state-dependent distributions using copulas. For the Bundesliga data considered, we find that the fitted HMMs comprise states which can be interpreted as a team showing different levels of control over a match. Our modelling framework enables inference related to causes of momentum shifts and team tactics, which is of much interest to managers, bookmakers, and sports fans.

scholarly journals Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

2021 ◽  
Author(s):  
Antonio Giovannetti ◽  
Gianluca Susi ◽  
Paola Casti ◽  
Arianna Mencattini ◽  
Sandra Pusil ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Ensemble Classification ◽  
The Novel ◽  
Ensemble Classifiers ◽  
Deep Convolutional Neural Networks ◽  
Early Signs ◽  
Data Representations ◽  
The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

scholarly journals Cinematic rendering of a burst sagittal suture caused by an occipito-frontal gunshot wound

2021 ◽  
Author(s):  
Dominic Gascho ◽  
Michael J. Thali ◽  
Rosa M. Martinez ◽  
Stephan A. Bolliger
Keyword(s):  
Gunshot Wound ◽  
Three Dimensional ◽  
The Novel ◽  
Sagittal Suture ◽  
Resonance Imaging ◽  
Cinematic Rendering ◽  
Reconstruction Methods ◽  
Dimensional Reconstruction ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

AbstractThe computed tomography (CT) scan of a 19-year-old man who died from an occipito-frontal gunshot wound presented an impressive radiating fracture line where the entire sagittal suture burst due to the high intracranial pressure that arose from a near-contact shot from a 9 mm bullet fired from a Glock 17 pistol. Photorealistic depictions of the radiating fracture lines along the cranial bones were created using three-dimensional reconstruction methods, such as the novel cinematic rendering technique that simulates the propagation and interaction of light when it passes through volumetric data. Since the brain had collapsed, depiction of soft tissue was insufficient on CT images. An additional magnetic resonance imaging (MRI) examination was performed, which enabled the diagnostic assessment of cerebral injuries.

scholarly journals Neurophysiological changes in the visuomotor network after practicing a motor task

2018 ◽  
Vol 120 (1) ◽  
pp. 239-249 ◽  
Author(s):  
James E. Gehringer ◽  
David J. Arpin ◽  
Elizabeth Heinrichs-Graham ◽  
Tony W. Wilson ◽  
Max J. Kurz
Keyword(s):  
Motor Learning ◽  
Motor Planning ◽  
Motor Task ◽  
Force Production ◽  
Oscillatory Activity ◽  
Matching Task ◽  
Force Output ◽  
Motor Action ◽  
Visuomotor Transformations ◽  
Target Matching

Although it is well appreciated that practicing a motor task updates the associated internal model, it is still unknown how the cortical oscillations linked with the motor action change with practice. The present study investigates the short-term changes (e.g., fast motor learning) in the α- and β-event-related desynchronizations (ERD) associated with the production of a motor action. To this end, we used magnetoencephalography to identify changes in the α- and β-ERD in healthy adults after participants practiced a novel isometric ankle plantarflexion target-matching task. After practicing, the participants matched the targets faster and had improved accuracy, faster force production, and a reduced amount of variability in the force output when trying to match the target. Parallel with the behavioral results, the strength of the β-ERD across the motor-planning and execution stages was reduced after practice in the sensorimotor and occipital cortexes. No pre/postpractice changes were found in the α-ERD during motor planning or execution. Together, these outcomes suggest that fast motor learning is associated with a decrease in β-ERD power. The decreased strength likely reflects a more refined motor plan, a reduction in neural resources needed to perform the task, and/or an enhancement of the processes that are involved in the visuomotor transformations that occur before the onset of the motor action. These results may augment the development of neurologically based practice strategies and/or lead to new practice strategies that increase motor learning. NEW & NOTEWORTHY We aimed to determine the effects of practice on the movement-related cortical oscillatory activity. Following practice, we found that the performance of the ankle plantarflexion target-matching task improved and the power of the β-oscillations decreased in the sensorimotor and occipital cortexes. These novel findings capture the β-oscillatory activity changes in the sensorimotor and occipital cortexes that are coupled with behavioral changes to demonstrate the effects of motor learning.

scholarly journals Activation State-Dependent Substrate Gating in Ca2+/Calmodulin-Dependent Protein Kinase II

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
D. E. Johnson ◽  
A. Hudmon
Keyword(s):  
Protein Kinase ◽  
Catalytic Domain ◽  
Intrinsic Property ◽  
Dependent Protein Kinase ◽  
Calcium Calmodulin Dependent ◽  
State Dependent ◽  
Protein Kinase Ii ◽  
Dependent Protein ◽  
Substrate Phosphorylation ◽  
The Brain

Calcium/calmodulin-dependent protein kinase II (CaMKII) is highly concentrated in the brain where its activation by the Ca2+sensor CaM, multivalent structure, and complex autoregulatory features make it an ideal translator of Ca2+signals created by different patterns of neuronal activity. We provide direct evidence that graded levels of kinase activity and extent of T287(T286αisoform) autophosphorylation drive changes in catalytic output and substrate selectivity. The catalytic domains of CaMKII phosphorylate purified PSDs much more effectively when tethered together in the holoenzyme versus individual subunits. Using multisubstrate SPOT arrays, high-affinity substrates are preferentially phosphorylated with limited subunit activity per holoenzyme, whereas multiple subunits or maximal subunit activation is required for intermediate- and low-affinity, weak substrates, respectively. Using a monomeric form of CaMKII to control T287autophosphorylation, we demonstrate that increased Ca2+/CaM-dependent activity for all substrates tested, with the extent of weak, low-affinity substrate phosphorylation governed by the extent of T287autophosphorylation. Our data suggest T287autophosphorylation regulates substrate gating, an intrinsic property of the catalytic domain, which is amplified within the multivalent architecture of the CaMKII holoenzyme.

Does Motor Learning Occur in the Spinal Cord?

1997 ◽  
Vol 3 (5) ◽  
pp. 287-294 ◽  
Author(s):  
V. Reggie Edgerton ◽  
Roland R. Roy ◽  
Ray De Leon Niranjala Tillakaratne ◽  
John A. Hodgson
Keyword(s):  
Spinal Cord ◽  
Motor Task ◽  
Lumbar Spinal Cord ◽  
Lumbosacral Spinal Cord ◽  
Patterns Of Use ◽  
The Neural Network ◽  
The Central Nervous System ◽  
Network Plasticity ◽  
Lumbar Spinal ◽  
The Brain

It is becoming clear that the plasticity of the sensory-motor networks of the adult mammalian lumbosacral spinal cord is much greater than and is more dependent on the specific patterns of use than has been previously assumed. Using a wide variety of experimental paradigms in which the lumbar spinal cord is isolated from the brain, it has been shown that the lumbosacral spinal cord can learn to execute stepping or standing more successfully if that specific task is practiced. It also appears that the sensory input associated with the motor task and/or the manner in which it is interpreted by the spinal cord are important components of the neural network plasticity. Early evidence suggests that several neurotransmitter systems in the spinal cord, to include glycinergic and GABAergic systems, adapt to repetitive use. These studies extend a growing body of evidence suggesting that memory and learning are widely distributed phenomena within the central nervous system. NEUROSCIENTIST 3:287–294, 1997

Effects of model types in observational learning on implicit sequential learning

2018 ◽  
Vol 71 (7) ◽  
pp. 1596-1606
Author(s):  
Kanji Tanaka ◽  
Katsumi Watanabe
Keyword(s):  
Reaction Time ◽  
Action Observation ◽  
Reaction Time Task ◽  
Test Session ◽  
Sequential Learning ◽  
Test Sequence ◽  
Learning Effects ◽  
Behavior Type ◽  
Movie Clip ◽  
Motor Representations

This study investigated whether implicit learning of sequence by observation occurred in a serial reaction time task and whether the learning effects were modulated by model behavioral type. In Experiment 1, we let 20 participants perform a sequence for 12 blocks and chose the best and worst performance models based on reaction time and errors. In Experiment 2, new observers viewed a movie clip chosen from the following three: the best model performing the sequential task in the first (the first six blocks) or second session (the last six blocks), or the worst model performing the task in the first session. Then, the observers performed the observed sequence, a test sequence and awareness test. We found that (1) implicit sequential learning occurred by observation regardless of model behavior type, (2) the learning effects were not susceptible to model behavior type and (3) speed index reflecting reaction time became larger even in the test session when the observers viewed the best model performing the second session. Overall, observers developed general motor representations through action–observation. In addition, their responses were also contagious; if the model performed the sequence faster, the observer might be able to perform the sequence faster.

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