AIMC Topic: Psychomotor Performance
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Working memory load-dependent changes in cortical network connectivity estimated by machine learning.
NeuroImage
May 1, 2020
Working memory engages multiple distributed brain networks to support goal-directed behavior and higher order cognition. Dysfunction in working memory has been associated with cognitive impairment in neuropsychiatric disorders. It is important to cha...
EO-MTRNN: evolutionary optimization of hyperparameters for a neuro-inspired computational model of spatiotemporal learning.
Biological cybernetics
Mar 17, 2020
For spatiotemporal learning with neural networks, hyperparameters are often set manually by a human expert. This is especially the case with multiple timescale networks that require a careful setting of the values of timescales in order to learn spat...
A systematic evaluation of the evidence for perceptual control theory in tracking studies.
Neuroscience and biobehavioral reviews
Feb 21, 2020
Perceptual control theory (PCT) proposes that perceptual inputs are controlled to intentional 'reference' states by hierarchical negative feedback control, evidence for which comes from manual tracking experiments in humans. We reviewed these experim...
fNIRS-GANs: data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy.
Journal of neural engineering
Feb 19, 2020
OBJECTIVE: Functional near-infrared spectroscopy (fNIRS) is expected to be applied to brain-computer interface (BCI) technologies. Since lengthy fNIRS measurements are uncomfortable for participants, it is difficult to obtain enough data to train cla...
A comparison of fMRI and behavioral models for predicting inter-temporal choices.
NeuroImage
Feb 18, 2020
In an inter-temporal choice (IteCh) task, subjects are offered a smaller amount of money immediately or a larger amount at a later time point. Here, we are using trial-by-trial fMRI data from 363 recording sessions and machine learning in an attempt ...
A neural circuit model for a contextual association task inspired by recommender systems.
Hippocampus
Feb 14, 2020
Behavioral data shows that humans and animals have the capacity to learn rules of associations applied to specific examples, and generalize these rules to a broad variety of contexts. This article focuses on neural circuit mechanisms to perform a con...
Skilled reaching tasks for head-fixed mice using a robotic manipulandum.
Nature protocols
Feb 7, 2020
Skilled forelimb behaviors are among the most important for studying motor learning in multiple species including humans. This protocol describes learned forelimb tasks for mice using a two-axis robotic manipulandum. Our device provides a highly comp...
Machine learning validation of EEG+tACS artefact removal.
Journal of neural engineering
Jan 24, 2020
OBJECTIVE: Electroencephalography (EEG) recorded during transcranial alternating current simulation (tACS) is highly desirable in order to investigate brain dynamics during stimulation, but is corrupted by large amplitude stimulation artefacts. Artef...
The Role of Robotic Path Assistance and Weight Support in Facilitating 3D Movements in Individuals With Poststroke Hemiparesis.
Neurorehabilitation and neural repair
Jan 20, 2020
. High-intensity repetitive training is challenging to provide poststroke. Robotic approaches can facilitate such training by unweighting the limb and/or by improving trajectory control, but the extent to which these types of assistance are necessary...
Assessing various sensorimotor and cognitive functions in people with epilepsy is feasible with robotics.
Epilepsy & behavior : E&B
Jan 7, 2020
BACKGROUND: Epilepsy is a common neurological disorder characterized by recurrent seizures, along with comorbid cognitive and psychosocial impairment. Current gold standards of assessment can quantify cognitive and motor performance, but may not capt...
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