Show Reference: "Bridging the gap between theories of sensory cue integration and the physiology of multisensory neurons"

Bridging the gap between theories of sensory cue integration and the physiology of multisensory neurons Nature Reviews Neuroscience, Vol. 14, No. 6. (20 June 2013), pp. 429-442, doi:10.1038/nrn3503 by Christopher R. Fetsch, Gregory C. DeAngelis, Dora E. Angelaki
    abstract = {The richness of perceptual experience, as well as its usefulness for guiding behaviour, depends on the synthesis of information across multiple senses. Recent decades have witnessed a surge in our understanding of how the brain combines sensory cues. Much of this research has been guided},
    author = {Fetsch, Christopher R. and DeAngelis, Gregory C. and Angelaki, Dora E.},
    day = {20},
    doi = {10.1038/nrn3503},
    issn = {1471-003X},
    journal = {Nature Reviews Neuroscience},
    keywords = {biology, cue-combination, multisensory-integration},
    month = jun,
    number = {6},
    pages = {429--442},
    posted-at = {2013-06-06 17:23:34},
    priority = {2},
    publisher = {Nature Publishing Group},
    title = {Bridging the gap between theories of sensory cue integration and the physiology of multisensory neurons},
    url = {},
    volume = {14},
    year = {2013}

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Multisensory integration is a way to reduce uncertainty. This is both a normative argument and it states the evolutionary advantage of using multisensory integration.

Fetsch et al. define cue combination as the `combination of multiple sensory cues' arising from the same event or object.

Ideal observer models of some task are mathematical models describing how an observer might achieve optimal results in that task under the given restrictions, most importantly under the given uncertainty.

Ideal observer models of cue integration were introduced in vision research but are now used in other uni-sensory tasks (auditory, somatosensory, proprioceptive and vestibular).

When the error distribution in multiple estimates of a world property on the basis of multiple cues is independent between cues, and Gaussian, then the ideal observer model is a simple weighting strategy.

MLE has been a successful model in many, but not all cue integration tasks studied.

One model which might go beyond MLE in modeling cue combination is `causal inference'.

There are two strands in multi-sensory research: mathematical modeling and modeling of neurophysiology.

According to Ma et al,'s work, computations in neurons doing multi-sensory integration should be additive or sub-additive. This is at odds with observed neurophysiology.

Stanford et al. studied single-neuron responses to cross-modal stimuli in their receptive fields. In contrast to previous studies, they systematically tried out different combinations of levels of intensity levels in different modalities.

Fetsch et al. explain the discrepancy between observed neurophysiology—superadditivity—and the normative solution to single-neuron cue integration proposed by Ma et al. using divisive normalization:

They propose that the network activity is normalized in order to keep neurons' activities within their dynamic range. This would lead to the apparent reliability-dependent weighting of responses found by Morgan et al. and superadditivity as described by Stanford et al.

Fetsch et al. acknowledge the similarity of their model with that of Ohshiro et al.

Fetsch et al. provide some sort of normative motivation to the model due to Ohshiro et al.