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The sigmoid squashing function used in Rowland et al.'s first model leads to inverse effectiveness: The sum of weak inputs generally falls into the supra-linear part of the sigmoid and thus produces a superadditive response.

AES neurons show an interesting form of the principle of inverse effectiveness: Cross-sensory in regions in which the unisensory component stimuli would evoke only a moderate response produce additive (or, superadditive?) responses. In contrast, Cross-sensory stimuli at the `hot spots' of a neuron tend to produce sub-additive responses.

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.

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.

My model is normative, performs optimally and it shows super-additivity (to be shown).

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.

Studies of single-neuron responses to multisensory stimuli have usually not explored the full dynamic range of inputs---they often have used near- or subthreshold stimulus intensities and thus usually found superadditive effects.

Input in Martin et al.'s model of multisensory integration in the SC replicates enhancement and, through the non-linear transfer function, superadditivity.

Neural responses in the sc to spatially and temporally coincident cross-sensory stimuli can be much stronger than responses to uni-sensory stimuli.

In fact, they can be much greater than the sum of the responses to either stimulus alone.

Stanford et al. state that superadditivity seems quite common in cases of multi-sensory enhancement.

Anastasio et al. present a model of the response properties of multi-sensory SC neurons which explains enhancement, depression, and super-addititvity using Bayes' rule: If one assumes that a neuron integrates its input to infer the posterior probability of a stimulus source being present in its receptive field, then these effects arise naturally.