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Cats, if raised in an environment in which the spatio-temporal relationship of audio-visual stimuli is artificially different from natural conditions, develop spatio-temporal integration of audio-visual stimuli accordingly. Their SC neurons develop preference to audio-visual stimuli with the kind of spatio-temporal relationship encountered in the environment in which they were raised.

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).

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.

Neural responses (in multi-sensory neurons) in the sc to spatially disparate cross-sensory stimuli is usually weaker than responses to uni-sensory stimuli.

Responses in multi-sensory neurons in the SC follow the so-called spatial principle.

Moving eyes, ears, or body changes the receptive field (in external space) in SC neurons wrt. stimuli in the respective modality.

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.

The model due to Cuppini et al. develops low-level multisensory integration (spatial principle) such that integration happens only with higher-level input.

In their model, Hebbian learning leads to sharpening of receptive fields, overlap of receptive fields, and Integration through higher-cognitive input.

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.