Hippocampus
51 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Hippocampus
Most implemented papers
Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network
The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans.
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture.
Machine learning for neural decoding
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods.
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
Many neurons in the brain, such as place cells in the rodent hippocampus, have localized receptive fields, i. e., they respond to a small neighborhood of stimulus space.
Dilated deeply supervised networks for hippocampus segmentation in MRI
Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer's Disease (AD).
Additive function approximation in the brain
We identify three specific advantages of sparsity: additive function approximation is a powerful inductive bias that limits the curse of dimensionality, sparse networks are stable to outlier noise in the inputs, and sparse random features are scalable.
Centroid Based Concept Learning for RGB-D Indoor Scene Classification
aliayub7/CBCL_RGBD
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BMVC 2020
Inspection of the centroids generated by our approach on RGB-D datasets leads us to propose a method for merging conceptually similar categories, resulting in improved accuracy for all approaches.
Cognitively-Inspired Model for Incremental Learning Using a Few Examples
To solve this problem, we propose a novel approach inspired by the concept learning model of the hippocampus and the neocortex that represents each image class as centroids and does not suffer from catastrophic forgetting.
Triple Memory Networks: a Brain-Inspired Method for Continual Learning
lywang3081/TMNs
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Continual acquisition of novel experience without interfering previously learned knowledge, i. e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting.
MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases
There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimers Disease (AD).
