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Nerve Net

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Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach.

Human brain mapping
Developmental dyslexia has been hypothesized to result from multiple causes and exhibit multiple manifestations, implying a distributed multidimensional effect on human brain. The disruption of specific white-matter (WM) tracts/regions has been obser...

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

NeuroImage
Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over ti...

Closed-Loop Modulation of the Pathological Disorders of the Basal Ganglia Network.

IEEE transactions on neural networks and learning systems
A generalized predictive closed-loop control strategy to improve the basal ganglia activity patterns in Parkinson's disease (PD) is explored in this paper. Based on system identification, an input-output model is established to reveal the relationshi...

Node Detection Using High-Dimensional Fuzzy Parcellation Applied to the Insular Cortex.

Neural plasticity
Several functional connectivity approaches require the definition of a set of regions of interest (ROIs) that act as network nodes. Different methods have been developed to define these nodes and to derive their functional and effective connections, ...

Effect of edge pruning on structural controllability and observability of complex networks.

Scientific reports
Controllability and observability of complex systems are vital concepts in many fields of science. The network structure of the system plays a crucial role in determining its controllability and observability. Because most naturally occurring complex...

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

PLoS computational biology
The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be ...

Reduction of Trial-to-Trial Perceptual Variability by Intracortical Tonic Inhibition.

Neural computation
Variability is a prominent characteristic of cognitive brain function. For instance, different trials of presentation of the same stimulus yield higher variability in its perception: subjects sometimes fail in perceiving the same stimulus. Perceptual...

Neural Network Spectral Robustness under Perturbations of the Underlying Graph.

Neural computation
Recent studies have been using graph-theoretical approaches to model complex networks (such as social, infrastructural, or biological networks) and how their hardwired circuitry relates to their dynamic evolution in time. Understanding how configurat...

Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.

Journal of computational neuroscience
Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studi...