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The in-vitro study of the rat intermediate SC by Lee and Hall did not find evidence for the long-range inhibitory/short-range excitatory connection pattern theorized by proponents of the neural-field theory of SC fixation.

Competitive learning can be implemented in ANN by strong, constant inhibitory connections between competing neurons.

Simple competitive neural learning with constant inhibitory connections between competing neurons leads to grandmother-type cells.

Simple competitive neural learning with constant inhibitory connections between competing neurons produces a code that facilitates further processing.

SIV neurons' activity can be inhibited by activity in the auditory FAES.

Dehner et al. speculate that the inhibitory influence of FAES activity on SIV activity is connected to modality-specific attention: According to that hypothesis, an auditory stimulus which leads to strong FAES activity will suppress activity in FAES and thus block out cortical somatosensory input to the SC.

There are inhibitory connections from deep SC to superficial SC (SGI to SGS).

The fact that no long-range inhibitory/short-range excitatory connection pattern were found in in-vitro study of the rat intermediate SC by Lee might also pose a problem for divisive-normalization as a modeling assumption for the SC.

Deco and Rolls introduce a system that uses a trace learning rule to learn recognition of more and more complex visual features in successive layers of a neural architecture. In each layer, the specificity of the features increases together with the receptive fields of neurons until the receptive fields span most of the visual range and the features actually code for objects. This model thus is a model of the development of object-based attention.

A localized visual stimulus can lengthen the response time to a target if the target stimulus appears somewhere too late after the first stimulus.

This is called `inhibition of return'.

The fact that average neural activity in cortex is decreased if spatial attention is on the location of a non-target out of a target and a non-target compared to when it is on the target supports the notion that inhibition plays an important role in stimulus selection.

Two superimposed visual stimuli of different orientation, one optimal for a given simple cell in visual cortex, the other sub-optimal but excitatory, can elicit a weaker response than just the optimal stimulus.

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.

The model due to Rowland and Stein does not consider the spatial properties of input or output. In reality, the same source of input—retina, LGN, association cortex may convey information about stimulus conditions from different regions in space and neurons at different positions in the SC react to different stimuli.

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).

Divisive normalization is probably implemented through (GABA-ergic) inhibition in some cases (fruitfly olfactory system). In others (V1), it seems to be implemented by different means.

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.

Omnipause neurons in the reticular formation tonically inhibit `the saccade-generation circuit'.

It seems unclear what is the original source of SC inhibition in preparation of anti-saccades. Munoz and Everling cite the supplementary eye fields (SEF), dorsolateral prefrontal cortex (DLPFC) as possible sources, and the substantia nigra pars reticulata (SNpr).

Short-range inhibition happens in the horseshoe crab compound eye: neighbouring receptor units inhibit each other.

Inhibitory connections can help stability in a network: they can broaden the range of connection parameters in which the network exhibits differentiated behavior.