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.⇒