Show Reference: "Implementing Bayes' Rule with Neural Fields"

Implementing Bayes' Rule with Neural Fields In Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008), pp. 228-237, doi:10.1007/978-3-540-87559-8_24 by Raymond H. Cuijpers, Wolfram Erlhagen
@inproceedings{cuijpers-and-erlhagen-2008,
    abstract = {Bayesian statistics is has been very successful in describing behavioural data on decision making and cue integration under noisy circumstances. However, it is still an open question how the human brain actually incorporates this functionality. Here we compare three ways in which Bayes rule can be implemented using neural fields. The result is a truly dynamic framework that can easily be extended by {non-Bayesian} mechanisms such as learning and memory.},
    address = {Berlin, Heidelberg},
    author = {Cuijpers, Raymond H. and Erlhagen, Wolfram},
    booktitle = {Proceedings of the 18th international conference on Artificial Neural Networks, Part II},
    doi = {10.1007/978-3-540-87559-8\_24},
    isbn = {978-3-540-87558-1},
    keywords = {bayes, neural-fields, population-coding},
    location = {Prague, Czech Republic},
    pages = {228--237},
    posted-at = {2011-08-12 17:36:50},
    priority = {2},
    publisher = {Springer-Verlag},
    series = {ICANN '08},
    title = {Implementing Bayes' Rule with Neural Fields},
    url = {http://dx.doi.org/10.1007/978-3-540-87559-8\_24},
    year = {2008}
}

See the CiteULike entry for more info, PDF links, BibTex etc.

Cuijpers and Erlhagen use neural fields to implement Bayes' rule for combining the activities of neural populations spatially encoding probability distributions.

Neural populations can compute and encode probability density functions for external variables.