Results 21 to 30 of about 367,087 (259)

A Two-Stage Neural Network for Sleep Stage Classification Based on Feature Learning, Sequence Learning, and Data Augmentation

IEEE Access, 2019
Sleep stage classification is a fundamental but cumbersome task in sleep analysis. To score the sleep stage automatically, this study presents a stage classification method based on a two-stage neural network.
Chenglu Sun, Jiahao Fan, Chen Chen, Wei Li, Wei Chen   +4 more
doaj   +1 more source

Implicit and explicit learning of a sequential postural weight-shifting task in young and older adults

Frontiers in Psychology, 2016
Sequence-specific postural motor learning in a target-directed weight-shifting task in twelve older and twelve young participants was assessed. In the implicit sequence learning condition participants performed a concurrent spatial cognitive task and in ...
Simone eCaljouw, Renee eVeldkamp, Claudine eLamoth   +2 more
doaj   +1 more source

Motor performance is not enhanced by daytime naps in older adults

Frontiers in Aging Neuroscience, 2016
The impact of sleep on motor learning in the aging brain was investigated using an experimental diurnal nap setup. As the brain ages several components of learning as well as motor performance change.
Winifried eBackhaus, Hanna eBraass, Thomas eRenne, Christian eGerloff, Friedhelm C Hummel, Friedhelm C Hummel   +5 more
doaj   +1 more source

Inferring an Observer’s Prediction Strategy in Sequence Learning Experiments

Entropy, 2020
Cognitive systems exhibit astounding prediction capabilities that allow them to reap rewards from regularities in their environment. How do organisms predict environmental input and how well do they do it? As a prerequisite to answering that question, we
Abhinuv Uppal, Vanessa Ferdinand, Sarah Marzen   +2 more
doaj   +1 more source

Relational Sequence Learning

, 2008
Sequential behavior and sequence learning are essential to intelligence. Often the elements of sequences exhibit an internal structure that can elegantly be represented using relational atoms. Applying traditional sequential learning techniques to such relational sequences requires one either to ignore the internal structure or to live with a ...
Kersting, Kristian, De Raedt, Luc, Gutmann, Bernd, Karwath, Andreas, Landwehr, Niels   +4 more
openaire   +2 more sources

Accuracy-based measures provide a better measure of sequence learning than reaction time-based measures.

Frontiers in Psychology, 2015
The Serial Reaction Time Task (SRTT) was designed to measure motor sequence learning and is widely used in many fields in cognitive science and neuroscience.
Kristi eUrry, Nicholas R. Burns, Irina eBaetu   +2 more
doaj   +1 more source

AIoT-Based Visual Anomaly Detection in Photovoltaic Sequence Data via Sequence Learning

Energies
Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to ...
Qian Wei, Hongjun Sun, Jingjing Fan, Guojun Li, Zhiguang Zhou   +4 more
doaj   +1 more source

A comprehensive account of sound sequence imitation in the songbird.

Frontiers in Computational Neuroscience, 2016
The amazing imitation capabilities of songbirds show that they can memorize sensory sequences and transform them into motor activities which in turn generate the original sound sequences.
Maren Westkott, Klaus R Pawelzik
doaj   +1 more source

Sleep-related offline improvements in gross motor task performance occur under free recall requirements

Frontiers in Human Neuroscience, 2016
Nocturnal sleep effects on memory consolidation following gross motor sequence learning were examined using a complex arm movement task. This task required participants to produce non-regular spatial patterns in the horizontal plane by successively ...
Andreas eMalangre, Klaus eBlischke
doaj   +1 more source

SPSNN: nth Order Sequence-Predicting Spiking Neural Network

IEEE Access, 2020
We introduce a means of harnessing spiking neural networks (SNNs) with rich dynamics as a dynamic hypothesis to learn complex sequences. The proposed SNN is referred to as nth order sequence-predicting SNN (n-SPSNN), which is capable of single-step ...
Dohun Kim, Vladimir Kornijcuk, Cheol Seong Hwang, Doo Seok Jeong   +3 more
doaj   +1 more source