Results 61 to 70 of about 65,094 (251)
The time lag in local field potential signals for the development of its Bayesian belief network
EURASIP Journal on Advances in Signal ProcessingPurpose The objective is to suggest time as an important variable to consider in the network model, specifically when discussing causality. Methods There is a consideration of the context of functional connectivity because of the time importance of ...
Victor H. B. Tsukahara +4 more
doaj +1 more source
Frontiers in Neuroscience, 2020 Just as the human brain works in a Bayesian manner to minimize uncertainty regarding external stimuli, a deafferented brain due to hearing loss attempts to obtain or “fill in” the missing auditory information, resulting in auditory phantom percepts (i.e.,
Sang-Yeon Lee +4 more
doaj +1 more source
Advanced Science, EarlyView.Skeleton‐oriented object segmentation (SKOOTS) introduces a new strategy for 3D mitochondrial instance segmentation by predicting explicit skeletons rather than relying on boundary cues. This approach enables robust analysis of densely packed organelles in large FIB‐SEM datasets.
Christopher J. Buswinka +3 more
wiley +1 more source
Computational Neuropsychology and Bayesian Inference
Frontiers in Human Neuroscience, 2018 Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research.
Thomas Parr +3 more
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Cognitive Trajectories from Preclinical Alzheimer's Disease to Dementia
Advanced Science, EarlyView.A continuous, multi‐domain characterization of cognitive decline across the Alzheimer's disease spectrum identifies when individual cognitive measures become abnormal. Episodic memory declines first, followed by executive function, language, processing speed, and visuospatial abilities, supporting improved clinical interpretation and optimized endpoint
Fredrik Öhman +3 more
wiley +1 more source
Bayesian statistics: relevant for the brain? [PDF]
Current Opinion in Neurobiology, 2014 Analyzing data from experiments involves variables that we neuroscientists are uncertain about. Efficiently calculating with such variables usually requires Bayesian statistics. As it is crucial when analyzing complex data, it seems natural that the brain would "use" such statistics to analyze data from the world.
openaire +2 more sources
SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics
Advanced Science, EarlyView.ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao +11 more
wiley +1 more source
How recent history affects perception: the normative approach and its heuristic approximation.
PLoS Computational Biology, 2012 There is accumulating evidence that prior knowledge about expectations plays an important role in perception. The Bayesian framework is the standard computational approach to explain how prior knowledge about the distribution of expected stimuli is ...
Ofri Raviv +2 more
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Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Advanced Science, EarlyView.Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
Advanced Science, EarlyView.STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu +5 more
wiley +1 more source