Show Reference: "Recurrent network for multisensory integration-identification of common sources of audiovisual stimuli"

Recurrent network for multisensory integration -Identification of common sources of audiovisual stimuli- Frontiers in Computational Neuroscience, Vol. 7, No. 101. (2013), doi:10.3389/fncom.2013.00101 by Itsuki Yamashita, Kentaro Katahira, Yasuhiko Igarashi, Kazuo Okanoya, Masato Okada
    abstract = {We perceive our surrounding environment by using different sense organs. However, it is not clear how the brain estimate information from our surroundings from the multisensory stimuli it receives. While Bayesian inference provides a normative account of the computational principle at work in the brain, it does not provide information on how the nervous system actually implements the computation. To provide an insight into how the neural dynamics are related to multisensory integration, we constructed a recurrent network model that can implement computations related to multisensory integration. Our model not only extracts information from noisy neural activity patterns, it also estimates a causal structure; i.e., it can infer whether the different stimuli came from the same source or different sources. We show that our model can reproduce the results of psychophysical experiments on spatial unity and localization bias which indicate that a shift occurs in the perceived position of a stimulus through the effect of another simultaneous stimulus. The experimental data have been reproduced in previous studies using Bayesian models. By comparing the Bayesian model and our neural network model, we investigated how the Bayesian prior is represented in neural circuits.},
    author = {Yamashita, Itsuki and Katahira, Kentaro and Igarashi, Yasuhiko and Okanoya, Kazuo and Okada, Masato},
    doi = {10.3389/fncom.2013.00101},
    journal = {Frontiers in Computational Neuroscience},
    number = {101},
    posted-at = {2013-07-26 14:16:56},
    priority = {2},
    title = {Recurrent network for multisensory integration -Identification of common sources of audiovisual stimuli-},
    url = {\_neuroscience/10.3389/fncom.2013.00101/abstract},
    volume = {7},
    year = {2013}

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Yamashita et al. modify Deneve et al.'s network by weakening divisive normalization and lateral inhibition. Thus, their network integrates localization if the disparity between localizations in simulated modalities is low, and maintains multiple hills of activation if disparity is high, thus accounting for the ventriloquism effect.

Yamashita et al. argue that, since whether or not two stimuli in different modalities with a certain disparity are integrated depends on the weight profiles in their network, a Bayesian prior is somehow encoded in these weights.