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: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciencesmathematicsapplied mathematicsstatistics and probabilitybayesian statistics
- natural sciencesbiological sciencesneurobiologycomputational neuroscience
- natural sciencescomputer and information sciencesdata sciencedata processing
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
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Topic(s)
Call for proposal
FP7-PEOPLE-2010-IEF
See other projects for this call
Funding Scheme
MC-IEF - Intra-European Fellowships (IEF)Coordinator
SW7 2AZ LONDON
United Kingdom