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Hippocampus

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A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer's Disease.

Journal of neuroscience methods
BACKGROUND: Hippocampus is one of the first structures affected by neurodegenerative diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). Hippocampal atrophy can be evaluated in terms of hippocampal volumes and shapes using ...

Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants.

NeuroImage
Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation m...

Phase relations of theta oscillations in a computer model of the hippocampal CA1 field: Key role of Schaffer collaterals.

Neural networks : the official journal of the International Neural Network Society
The hippocampal theta rhythm (4-12 Hz) is one of the most important electrophysiological processes in the hippocampus, it participates in cognitive hippocampal functions, such as navigation in space, novelty detection, and declarative memory. We use ...

Hippocampus Segmentation Based on Iterative Local Linear Mapping With Representative and Local Structure-Preserved Feature Embedding.

IEEE transactions on medical imaging
Hippocampus segmentation plays a significant role in mental disease diagnoses, such as Alzheimer's disease, epilepsy, and so on. Patch-based multi-atlas segmentation (PBMAS) approach is a popular method for hippocampus segmentation and has achieved a...

Unsupervised and real-time spike sorting chip for neural signal processing in hippocampal prosthesis.

Journal of neuroscience methods
BACKGROUND: Damage to the hippocampus will result in the loss of ability to form new long-term memories and cognitive disorders. At present, there is no effective medical treatment for this issue. Hippocampal cognitive prosthesis is proposed to repla...

Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis.

PloS one
We present a biologically motivated model for visual self-localization which extracts a spatial representation of the environment directly from high dimensional image data by employing a single unsupervised learning rule. The resulting representation...

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...

Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning.

IEEE transactions on medical imaging
Many medical image segmentation methods are based on the supervised classification of voxels. Such methods generally perform well when provided with a training set that is representative of the test images to the segment. However, problems may arise ...

A memristive plasticity model of voltage-based STDP suitable for recurrent bidirectional neural networks in the hippocampus.

Scientific reports
Memristive systems have gained considerable attention in the field of neuromorphic engineering, because they allow the emulation of synaptic functionality in solid state nano-physical systems. In this study, we show that memristive behavior provides ...