Show Reference: "Neurocomputational approaches to modelling multisensory integration in the brain: A review"

Neurocomputational approaches to modelling multisensory integration in the brain: A review Neural Networks (August 2014), doi:10.1016/j.neunet.2014.08.003 by Mauro Ursino, Cristiano Cuppini, Elisa Magosso
@article{ursino-et-al-2014,
    author = {Ursino, Mauro and Cuppini, Cristiano and Magosso, Elisa},
    citeulike-article-id = {13337733},
    citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.neunet.2014.08.003},
    doi = {10.1016/j.neunet.2014.08.003},
    issn = {08936080},
    journal = {Neural Networks},
    keywords = {model, multisensory-integration, sc},
    month = aug,
    posted-at = {2014-08-26 10:24:37},
    priority = {2},
    title = {Neurocomputational approaches to modelling multisensory integration in the brain: A review},
    url = {http://dx.doi.org/10.1016/j.neunet.2014.08.003},
    year = {2014},
    pages = {783--810},
    volume = {15},
    number = {4}
}

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Activity in the auditory cortex is modulated by visual stimuli.

V1 is influenced by auditory stimuli (in different ways).

Ursino et al. divide models of multisensory integration into three categories:

  1. Bayesian models (optimal integration etc.),
  2. neuron and network models,
  3. models on the semantic level (symbolic models).

The existence of inverse effectiveness has been questioned.

Multisensory integration in cortex has been studied less than in the midbrain, but there is work on that.

According to Ursino et al., there are two theories about the benefit of multisensory convergence at lower levels of cortical processing: One is that convergence helps resolve ambiguity and improves reliability. The other theory is that it helps predict perceptions.

There is multisensory integration in areas typically considered unisensory, eg. primary and secondary auditory cortex.