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Application of chaos in a recurrent neural network to control in ill-posed problems: a novel autonomous robot arm.

Biological cybernetics
Inspired by a viewpoint that complex/chaotic dynamics would play an important role in biological systems including the brain, chaotic dynamics introduced in a recurrent neural network was applied to robot control in ill-posed situations. By computer ...

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.

Nature neuroscience
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis ca...

Estimation of neural connections from partially observed neural spikes.

Neural networks : the official journal of the International Neural Network Society
Plasticity is one of the most important properties of the nervous system, which enables animals to adjust their behavior to the ever-changing external environment. Changes in synaptic efficacy between neurons constitute one of the major mechanisms of...

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks.

Neural networks : the official journal of the International Neural Network Society
Biological neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifelong learning. The interplay of these elements leads to the emergence of biological intelligence. Inspired by such intricate ...

Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients.

Acta psychiatrica Scandinavica
OBJECTIVE: This study determined the clinical utility of an fMRI classification algorithm predicting medication-class of response in patients with challenging mood diagnoses.

TractSeg - Fast and accurate white matter tract segmentation.

NeuroImage
The individual course of white matter fiber tracts is an important factor for analysis of white matter characteristics in healthy and diseased brains. Diffusion-weighted MRI tractography in combination with region-based or clustering-based selection ...

Artificial intelligence in retina.

Progress in retinal and eye research
Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehe...

Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia.

NeuroImage
This work presents a novel approach to finding linkage/association between multimodal brain imaging data, such as structural MRI (sMRI) and functional MRI (fMRI). Motivated by the machine translation domain, we employ a deep learning model, and consi...

Diagnostic model for attention-deficit hyperactivity disorder based on interregional morphological connectivity.

Neuroscience letters
Previous brain morphology-related diagnostic models for attention-deficit hyperactivity disorder (ADHD) were based on regional features. However, building a model of individual interregional morphological connectivity is a challenging task. This stud...

Deep(er) Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptabl...