[Comp-neuro] "Bayesian Networks in Neuroscience" (Research Topic
for Frontiers in Computational Neuroscience )
pedro.larranaga at fi.upm.es
Fri Jan 3 12:38:21 CET 2014
We would like to advertise our Research Topic for Frontiers in
Computational Neuroscience (impact factor 2.5) called "Bayesian Networks
See below a brief description and please visit our website:***
We are looking forward to receiving your abstracts!
Best wishes and happy new year!
Concha Bielza and Pedro Larranaga, Technical University of Madrid, Spain
*"Bayesian Networks in Neuroscience"*
Bayesian networks are a type of probabilistic graphical models that
represent the joint probability distribution over a set of random
variables by means of a directed acyclic graph and a list of conditional
probabilities. The directed acyclic graph shows the conditional
independencies among triplets of variables, allowing for sparse
representations of the joint probability distribution. The Bayesian
network model can be learnt automatically from data, by means of
structure learning algorithms, or alternatively can be given by an
expert in the domain to be modelled. Once the model is obtained it
constitutes an efficient and effective tool for reasoning. This
reasoning is carried out by exact or approximate inference algorithms
that propagate the given evidence through the graphical structure.
The discovery of relationships among entities and the inference
capabilities of Bayesian networks place them as an appropriate
methodology for modelling the underlying uncertainty in neuroscience at
three different levels of resolution and with any kind of neuronal
characteristics (morphological, electrophysiological, and genetic):
a) Microscopic: spine, synapse, neuron, population of them.
b) Macroscopic: temporal and causal relationships among different brain
regions from neuroimaging data (fMRIS, MEG, EEG,...).
c) Clinical: diagnosis, prognosis, and prediction of dementia
development in different neurodegenerative diseases: Parkinson,
This Research Topic aims to receive contributions from researchers from
different backgrounds who are either developing new inference and/or
learning algorithms for Bayesian networks motivated by neuroscience
problems, or applying existing methodologies to new data for the
understanding of brain structure, function, and dynamics.
Deadline for abstract submission: January 13, 2014
Deadline for full article submission: June 02, 2014
Prof. Pedro Larrañaga
Department of Artificial Intelligence
Technical University of Madrid
Campus de Montegancedo, s/n
28660 Boadilla del Monte
tel: +34 91 336 7443
fax: +34 91 352 4819
I try to answer my email quickly,
but I normally check it only once a day.
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