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Biological Mechanisms for Bayesian Inference


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.

[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.

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

Call for proposal

See other projects for this call

Funding Scheme

MC-IEF - Intra-European Fellowships (IEF)


South Kensington Campus Exhibition Road
SW7 2AZ London
United Kingdom
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 193 849,60
Administrative Contact
Brooke Alasya (Ms.)