The robust analysis of neural signals is a challenging problem. Here, we contribute a convolutional neural network (CNN) for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm. We measure electroencephalogram (EEG)...
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstra...
Neural networks : the official journal of the International Neural Network Society
Feb 20, 2017
Wearable devices, such as smart glasses and watches, allow for continuous recording of everyday life in a real world over an extended period of time or lifelong. This possibility helps better understand the cognitive behavior of humans in real life a...
OBJECTIVE: Classifier transfers usually come with dataset shifts. To overcome dataset shifts in practical applications, we consider the limitations in computational resources in this paper for the adaptation of batch learning algorithms, like the sup...
In many realistic networks, the edges representing the interactions between nodes are time varying. Evidence is growing that the complex network that models the dynamics of the human brain has time-varying interconnections, that is, the network is ev...
Some microstructure parameters, such as permeability, remain elusive because mathematical models that express their relationship to the MR signal accurately are intractable. Here, we propose to use computational models learned from simulations to est...
Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. ...
The ability to generalize over naturally occurring variation in cues indicating food or predation risk is highly useful for efficient decision-making in many animals. Honeybees have remarkable visual cognitive abilities, allowing them to classify vis...
Neural networks : the official journal of the International Neural Network Society
Jan 28, 2017
Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new ...
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