AIMC Topic: Hippocampus
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DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease.
NeuroImage
Aug 24, 2021
Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. Howe...
Multiscale representations of community structures in attractor neural networks.
PLoS computational biology
Aug 23, 2021
Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequent...
Interpreting wide-band neural activity using convolutional neural networks.
eLife
Aug 2, 2021
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the repres...
Robot navigation as hierarchical active inference.
Neural networks : the official journal of the International Neural Network Society
May 10, 2021
Localization and mapping has been a long standing area of research, both in neuroscience, to understand how mammals navigate their environment, as well as in robotics, to enable autonomous mobile robots. In this paper, we treat navigation as inferrin...
A machine learning approach to screen for preclinical Alzheimer's disease.
Neurobiology of aging
May 4, 2021
Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on F-florbetapir and F-fluorodeox...
Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus.
Trends in cognitive sciences
Apr 24, 2021
Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training A...
Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps.
Nature communications
Apr 22, 2021
Cognitive maps are mental representations of spatial and conceptual relationships in an environment, and are critical for flexible behavior. To form these abstract maps, the hippocampus has to learn to separate or merge aliased observations appropria...
Flexible modulation of sequence generation in the entorhinal-hippocampal system.
Nature neuroscience
Apr 12, 2021
Exploration, consolidation and planning depend on the generation of sequential state representations. However, these algorithms require disparate forms of sampling dynamics for optimal performance. We theorize how the brain should adapt internally ge...
Uncertainty-aware temporal self-learning (UATS): Semi-supervised learning for segmentation of prostate zones and beyond.
Artificial intelligence in medicine
Apr 10, 2021
Various convolutional neural network (CNN) based concepts have been introduced for the prostate's automatic segmentation and its coarse subdivision into transition zone (TZ) and peripheral zone (PZ). However, when targeting a fine-grained segmentatio...
Neurovascular multiparametric MRI defines epileptogenic and seizure propagation regions in experimental mesiotemporal lobe epilepsy.
Epilepsia
Apr 5, 2021
OBJECTIVE: Improving the identification of the epileptogenic zone and associated seizure-spreading regions represents a significant challenge. Innovative brain-imaging modalities tracking neurovascular dynamics during seizures may provide new disease...
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