Objective
A growing body of experimental evidence suggests that animals, and in particular humans, are
capable of inferring knowledge about reality from uncertain or incomplete data in a way that
is, according to Bayes’ theorem, mathematically optimal [1, 2]. The case that the brain is, at
some level, a Bayesian inference machine was made much stronger when Ma et al. [3] recently
described a mechanism whereby a neural network could indeed store and manipulate
probability distributions – a mechanism that reduces Bayes’ theorem to a sum [4]. We shall
bring together tools and recent advances from in the fields of Complex Networks [5] and
Computational Neuroscience [6] with a view to: a) designing methods and algorithms for tasks
such as grammar inference or network routing; b) putting forward neural network models
which are capable of integrating Bayesian inference with other necessary brain functions, such
as working memory or information processing; and c) exploring, in greater detail, how
Bayesian inference could be carried out in realistic biological settings.
References
[1] M.O. Ernst, M.S. Banks, N. Models, and S. Thresholds, Humans integrate visual and haptic
information in a statistically optimal fashion, Nature, 415, 429-33 (2002).
[2] T. Yang and M.N. Shadlen, Probabilistic reasoning by neurons, Nature, 447, 1075-80 (2007).
[3] W.J. Ma, J.M. Beck, P.E. Latham, and A. Pouget, Bayesian inference with probabilistic
population codes, Nature Neurosci., 9, 1432-8 (2006).
[4] J.M. Beck, W.J. Ma, R. Kiani, T. Hanks, A.K. Churchland, J. Roitman, M.N. Shadlen, P.E.
Latham, and A. Pouget, Probabilistic population codes for Bayesian decision making, Neuron,
60, 1142-52(2008).
[5] S. Johnson, J.J. Torres, J. Marro, and M.A. Muñoz, Entropic origin of disassortativity in
complex networks, Phys. Rev. Lett.. 104, 108702 (2010)
[6] S. Johnson, J. Marro, and J.J. Torres, Cluster Reverberation: a mechanism for robust working
memory without synaptic learning, submitted.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences mathematics applied mathematics statistics and probability bayesian statistics
- natural sciences biological sciences neurobiology computational neuroscience
- natural sciences computer and information sciences data science data processing
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
FP7-PEOPLE-2010-IEF
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Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
SW7 2AZ London
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.