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Fitting of dynamic recurrent neural network models to sensory stimulus-response data.

Journal of biological physics
We present a theoretical study aiming at model fitting for sensory neurons. Conventional neural network training approaches are not applicable to this problem due to lack of continuous data. Although the stimulus can be considered as a smooth time-de...

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score.

JAMA neurology
IMPORTANCE: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings.

Sensory cortex is optimized for prediction of future input.

eLife
Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimized to represent features in the recent sensory pas...

Predicting similarity judgments in intertemporal choice with machine learning.

Psychonomic bulletin & review
Similarity models of intertemporal choice are heuristics that choose based on similarity judgments of the reward amounts and time delays. Yet, we do not know how these judgments are made. Here, we use machine-learning algorithms to assess what factor...

Representing and querying now-relative relational medical data.

Artificial intelligence in medicine
Temporal information plays a crucial role in medicine. Patients' clinical records are intrinsically temporal. Thus, in Medical Informatics there is an increasing need to store, support and query temporal data (particularly in relational databases), i...

Robotic finger perturbation training improves finger postural steadiness and hand dexterity.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
The purpose of the study was to understand the effect of robotic finger perturbation training on steadiness in finger posture and hand dexterity in healthy young adults. A mobile robotic finger training system was designed to have the functions of hi...

Sensorimotor Robotic Measures of tDCS- and HD-tDCS-Enhanced Motor Learning in Children.

Neural plasticity
Transcranial direct-current stimulation (tDCS) enhances motor learning in adults. We have demonstrated that anodal tDCS and high-definition (HD) tDCS of the motor cortex can enhance motor skill acquisition in children, but behavioral mechanisms remai...

Recognition of early stage thigmotaxis in Morris water maze test with convolutional neural network.

PloS one
The Morris water maze test (MWM) is a useful tool to evaluate rodents' spatial learning and memory, but the outcome is susceptible to various experimental conditions. Thigmotaxis is a commonly observed behavioral pattern which is thought to be relate...

Mittag-Leffler synchronization of fractional neural networks with time-varying delays and reaction-diffusion terms using impulsive and linear controllers.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a fractional-order neural network system with time-varying delays and reaction-diffusion terms. We first develop a new Mittag-Leffler synchronization strategy for the controlled nodes via impulsive controllers. Using the fra...

Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes.

Current biology : CB
Even with great advances in machine vision, animals are still unmatched in their ability to visually search complex scenes. Animals from bees [1, 2] to birds [3] to humans [4-12] learn about the statistical relations in visual environments to guide a...