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The model of the SC due to Cuppini et al. reproduces development of

  1. multi-sensory neurons
  2. multi-sensory enhancement
  3. intra-modality depression
  4. super-additivity
  5. inverse effectiveness

The existence of inverse effectiveness has been questioned.

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.

In Anastasio et al. use their model to explain enhancement and the principle of inverse effectiveness.

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.

Enhancement is greatest for weak stimuli and least for strong stimuli. This is called inverse effectiveness.

The ANN model of multi-sensory integration in the SC due to Ohshiro et al. manages to replicate a number of physiological finding about the SC:

  • inverse effectiveness,
  • long-range inhibition and
  • short-range activation,
  • multisensory integration,
  • different tuning to modalities between neurons,
  • weighting of stimuli from different modalities.

It does not learn and it has no probabilistic motivation.

Bauer and Wermter use the algorithm they proposed to model multi-sensory integration in the SC. They show that it can learn to near-optimally integrate noisy multi-sensory information and reproduces spatial register of sensory maps, the spatial principle, the principle of inverse effectiveness, and near-optimal audio-visual integration in object localization.