Show Reference: "A model of the temporal dynamics of multisensory enhancement"

Rowland, B. A., Stein, B. E., Dec. 2013. A model of the temporal dynamics of multisensory enhancement. Neuroscience & Biobehavioral Reviews.
@article{rowland-and-stein-2014,
    author = {Rowland, Benjamin A. and Stein, Barry E.},
    citeulike-article-id = {12922517},
    citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.neubiorev.2013.12.003},
    doi = {10.1016/j.neubiorev.2013.12.003},
    issn = {01497634},
    journal = {Neuroscience \& Biobehavioral Reviews},
    keywords = {biology, sc, spiking, subadditivity, superadditivity},
    month = dec,
    posted-at = {2014-01-16 09:52:58},
    priority = {2},
    title = {A model of the temporal dynamics of multisensory enhancement},
    url = {http://dx.doi.org/10.1016/j.neubiorev.2013.12.003},
    year = {2013},
    volume = {41},
    number = {0},
    pages = {78--84}
}

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Neurons in the deep SC which show an enhancement in response to multisensory stimuli peak earlier.

The response profiles have superadditive, additive, and subadditive phases: Even for cross-sensory stimuli whose unisensory components are strong enough to elicit only an additive enhancement of the cumulated response, the response is superadditive over parts of the time course.

The temporal time course of neural integration in the SC reveals considerable non-linearity: early on, neurons seem to be super-additive before later settling into an additive or sub-additive mode of computation.

The leaky-integrate-and-fire model due to Rowland and Stein models a single multisensory SC neuron receiving input from a number of sensory, cortical, and sub-cortical sources.

Each of the sources is modeled as a single input to the SC neuron.

Local inhibitory interaction between neurons in multi-sensory trials is modeled by a single time-variant subtractive term which sets in shortly after the actual sensory input, thus not influencing the first phase of the response after stimulus onset.

Rowland and Stein focus on the temporal dynamics of multisensory integration.

Rowland and Stein's goal is only to generate neural responses like those observed in real SC neurons with realistic biological constraints. The model does not give any explanation of neural responses on the functional level.

The network characteristics of the SC are modeled only very roughly by Rowland and Stein's model.

The model due to Rowland and Stein manages to reproduce the nonlinear time course of neural responses to, and enhancement in magnitude and inverse effectiveness in multisensory integration in the SC.

Since the model does not include spatial properties, it does not reproduce the spatial principle (ie. no depression).