Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In this keynote talk we will pinpoint a number of neuroscience problems that can be addressed using Bayesian networks. In neuroanatomy, we will show computer simulation models of dendritic trees and classification of neuron types, both based on morphological features. In neurology, we will present the search for genetic biomarkers in Alzheimer's disease and the prediction of health-related quality of life in Parkinson's disease. Most of these challenging problems posed by neuroscience involve new Bayesian network designs that can cope with multiple class variables, small sample sizes, or labels annotated by several experts
There has been a growing interest during recent years in connectomics, which is the study of interco...
When reasoning in the presence of uncertainty there is a unique and self consistent set of rules for...
Bayesian inference has emerged as a general framework that captures how organisms make decisions und...
Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In...
Studying interactions between different brain regions or neural components is crucial in understandi...
Neuronal morphology is hugely variable across brain regions and species, and their classification st...
International audienceNetwork connectivity fingerprints are among today's best choices to obtain a f...
Neuroscientists have shown increased interest in knowing interactions among brain regions activated ...
We employ the language of Bayesian networks to systematically construct gene-regulation topologies f...
Neuron morphology is crucial for neuronal connectivity and brain information processing. Computation...
Bayesian networks (BN) have recently experienced increased interest and diverse applications in nume...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...
Item does not contain fulltextThis chapter provides an introduction to Bayesian models and their app...
A class-bridge decomposable multidimensional Gaussian net- work is presented as an interpretable an...
In this review we consider how Bayesian logic can help neuroscientists to understand behaviour and b...
There has been a growing interest during recent years in connectomics, which is the study of interco...
When reasoning in the presence of uncertainty there is a unique and self consistent set of rules for...
Bayesian inference has emerged as a general framework that captures how organisms make decisions und...
Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In...
Studying interactions between different brain regions or neural components is crucial in understandi...
Neuronal morphology is hugely variable across brain regions and species, and their classification st...
International audienceNetwork connectivity fingerprints are among today's best choices to obtain a f...
Neuroscientists have shown increased interest in knowing interactions among brain regions activated ...
We employ the language of Bayesian networks to systematically construct gene-regulation topologies f...
Neuron morphology is crucial for neuronal connectivity and brain information processing. Computation...
Bayesian networks (BN) have recently experienced increased interest and diverse applications in nume...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...
Item does not contain fulltextThis chapter provides an introduction to Bayesian models and their app...
A class-bridge decomposable multidimensional Gaussian net- work is presented as an interpretable an...
In this review we consider how Bayesian logic can help neuroscientists to understand behaviour and b...
There has been a growing interest during recent years in connectomics, which is the study of interco...
When reasoning in the presence of uncertainty there is a unique and self consistent set of rules for...
Bayesian inference has emerged as a general framework that captures how organisms make decisions und...