[Comp-neuro] "Bayesian Networks in Neuroscience" (Research Topic for Frontiers in Computational Neuroscience )

Pedro Larrañaga pedro.larranaga at fi.upm.es
Fri Jan 3 12:38:21 CET 2014


Dear all,

We would like to advertise our Research Topic for Frontiers in 
Computational Neuroscience (impact factor 2.5) called "Bayesian Networks 
in Neuroscience".
See below  a brief description and please visit our website:***
*

http://www.frontiersin.org/Computational_Neuroscience/researchtopics/Bayesian_networks_in_neuroscie/2255 
<http://www.frontiersin.org/neuroanatomy/researchtopics/quantitative_analysis_of_neuro/2028>

We are looking forward to receiving your abstracts!


Best wishes and happy new year!

Concha Bielza and Pedro Larranaga, Technical University of Madrid, Spain

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*"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, 
Alzheimer, Huntington,...

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
Madrid
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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