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.  recently
described a mechanism whereby a neural network could indeed store and manipulate
probability distributions – a mechanism that reduces Bayes’ theorem to a sum . We shall
bring together tools and recent advances from in the fields of Complex Networks  and
Computational Neuroscience  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.
 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).
 T. Yang and M.N. Shadlen, Probabilistic reasoning by neurons, Nature, 447, 1075-80 (2007).
 W.J. Ma, J.M. Beck, P.E. Latham, and A. Pouget, Bayesian inference with probabilistic
population codes, Nature Neurosci., 9, 1432-8 (2006).
 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,
 S. Johnson, J.J. Torres, J. Marro, and M.A. Muñoz, Entropic origin of disassortativity in
complex networks, Phys. Rev. Lett.., 104, 108702 (2010)
 S. Johnson, J. Marro, and J.J. Torres, Cluster Reverberation: a mechanism for robust working
memory without synaptic learning, submitted.
Field of science
- /natural sciences/computer and information sciences/data science/data processing
- /natural sciences/computer and information sciences/artificial intelligence/computational intelligence
- /natural sciences/biological sciences/neurobiology/computational neuroscience
- /natural sciences/mathematics/applied mathematics/statistics and probability/bayesian statistics
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