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Cuppini et al. present a model of the SC that exhibits many of the properties regarding neural connectivity, electrophysiology, and development that have been found experimentally in nature.

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 model due to Cuppini et al. comprises distinct neural populations for

  1. anterior ectosylvian sulcus (AES) and auditory subregion of AES (FAES)
  2. inhibitory interneurons between AES/FAES and SC
  3. space-coded ascending inputs (visual, auditory) to the SC
  4. inhibitory ascending interneurons
  5. (potentially) multi-sensory SC neurons.

The model due to Cuppini et al. does not need neural multiplication to implement superadditivity or inverse effectiveness. Instead, it exploits the sigmoid transfer function in multi-sensory neurons: due to this sigmoid transfer function and due to less-than-unit weights between input and multi-sensory neurons, weak stimuli that fall into the low linear regions of input neurons evoke less than linear responses in multi-sensory neurons. However, the sum of two such stimuli (from different modalities) can be in their linear range and thus the result can be much greater than the sum of the individual responses.

Through lateral connections, a Hebbian learning rule, and approximate initialization, Cuppini et al. manage to learn register between sensory maps. This can be seen as an implementation of a SOM.

Cuppini et al. use mutually inhibitive, modality-specific inhibition (inhibitory inter-neurons that get input from one modality and inhibit inhibitory interneurons receiving input from different modalities) to implement a winner-take-all mechanism between modalities; this leads to a visual (or auditory) capture effect without functional multi-sensory integration.

Their network model builds upon their earlier single-neuron model.

Not sure about the biological motivation of this. Also: it would be interesting to know if functional integration still occurs.

Cuppini et al. do not evaluate their model's performance (comparability to cat/human performance, optimality...)

The model due to Cuppini et al. is inspired only by observed neurophysiology; it has no normative inspiration.

Ravulakollu et al. loosely use the super colliculus as a metaphor for their robotic visual-auditory localization.

Ravulakollu et al. argue against SOMs and for radial basis functions (RBF) for combining stimuli (for reasons I don't quite understand).

The time it takes to elicit a visual cortical response plus the time to elicit a saccade from cortex (FEF) is longer than the time it takes for humans to orient towards faces.

Nakano et al. take this as further evidence for a sub-cortical (retinotectal) route of face detection.

The superior colliculus does not receive any signals from short-wavelength cones (S-cones) in the retina.

Nakano et al. presented an image of either a butterfly or a neutral or emotional face to their participants. The stimuli were either grayscale or color-scale images, where color-scale images were isoluminant and only varied in their yellow-green color values. Since information from S-cones does not reach the superior colliculus, these faces were presumably only processed in visual cortex.

Nakano et al. found that their participants reacted to gray-scale emotional faces faster than to gray-scale neutral faces and to gray-scale faces faster than to gray-scale butterflies. Their participants reacted somewhat faster to color-scale faces than to color-scale butterflies, but this effect was much smaller than for gray-scale images. Also, the difference in reaction time to color-scale emotional faces was not significantly different from that to color-scale neutral faces.

Nakano et al. take this as further evidence of sub-cortical face detection and in particular of emotional sub-cortical face detection.

Many neurons in the cat and monkey deep SC are uni-sensory.

There is a theory called the `premotor theory of visual attention' which posits that activity that can ultimately lead to a saccade can also facilitate processing of stimuli in those places the saccade will/would go to.

The SC is involved in a lot of things.

The SC is involved in tongue snapping in toads.

Satou et al. assume there is `switch-like' behavior in toad tounge snapping and predator avoidance.

According to Satou et al., the optic tectum is where the decision to snap the tongue (at insects).

Tongue snapping can be evoked in toads by electrostimulation of neurons in their optic tectum.

The `snapping-evoking area' in the toad optic tectum is in the lateral/vetrolateral part of the OT.

Visual receptive fields in the superficial hamster SC do not vary substantially in RF size with RF eccentricity.

Visual receptive field sizes change in the deep SC with eccentricity, they do not in the superficial hamster SC.

Sensory maps and their registration across modalities has been demonstrated in mice, cats, monkeys, guinea pigs, hamsters, barn owls, and iguanas.

According to Johnson and Morton, there are two visual pathways for face detection: the primary cortical pathway and one through SC and pulvinar.

The cortical pathway is called CONLEARN and is theorized to be plastic, whereas the sub-cortical pathway is called CONSPEC and is thought to be fixed and genetically predisposed to detect conspecific faces.

The SC has been suggested to be involved in social interaction.

Masking visual face stimuli can evoke responses in sc, pulvinar, and amygdala.

The right amygdala responded differently to masked than to unmasked stimuli, while the left did not in Morris et al.'s experiments.

Law and Constantine-Paton transplanted eye primordia between tadpoles to create three-eyed frogs.

The additional eyes connected to the frogs' contralateral tecta and created competition of inputs which is not usually present in frogs (where the optic chiasm is perfect).

The result were tecta in which alternating stripes are responsive to input from different eyes.

Similar results result if one of the tecta is removed and both natural retinae project to the remaining tectum.

Excitatory burst neurons (EBNs) in the paramedian pontine reticular formation (pprf) (pons) initiate saccades.

These neurons receive direct excitatory input from SC and inhibitory input from the nucleus raphe interpositus (RIP) (brainstem).

Wang et al. provide evidence that SC might during saccades turn of RIP inhibition through the central mesencephalic reticular formation (cMRF) while it drives EBNs.

Electrical stimulation of the SC can evoke motor behavior.

Electrical stimulation of the cat SC can evoke saccades.

Typically, these saccades go into that general direction in which natural stimuli would lead to activation in the area that was electrically stimulated.

The `foveation hypothesis' states that the SC elicits saccades which foveate the stimuli activating it for further examination.

The SC may play a major role in the selection of stimuli—as saccade targets or as reaching targets.

The activity of an SC neuron is proportional to the probability of the endpoint of a saccade being in that neuron's receptive field.

A possible ascending pathway from SC to visual cortex through the pulvinar nuclei (pulvinar) may be responsible for the effect of SC activity on visual processing in the cortex.

There may be an indirect ascending pathway from intermediate SC to the thalamic reticular nucleus.

Activity of the SC affects activity in cortical regions.

It has been found that stimulating supposed motor neurons in the SC facilitates visual processing in the part of visual cortex whose receptive field is the same as that of the SC stimulated neurons.

There are two hypotheses about saccadic control:

  • either there are separate, competing fixation and motion systems,
  • or there is competition between neurons in the SC coding for different motions.

According to Casteau and Vitu, a fixation population, if it exists, is probably located not in the SC but in the brainstem omnipause region.

According to Casteau and Vitu, a fixation population, if it exists, is probably located not in the SC but in the brainstem omnipause region.

Omnipause neurons (OPN) receive input from neurons across the SC, though more strongly from the rostral part.

Distractors close to a saccade target do not seem to affect saccade latency but change their landing sites.

Casteau and Vitu see the lack of a change in saccade latency due to distractors close to the saccade target as evidence against the lateral-inhibition theory of saccade generation.

Casteau and Vitu's results seem to show that it's not proximity between target and distractor but the ratio of their excentricities that saccade delay is dependent of.

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.

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.

Response properties in mouse superficial SC neurons are not strongly influenced by experience.

How strongly SC neurons' development depends on experience (and how strongly well they are developed after birth) is different from species to species, so just because the superficial mouse SC is developed at birth, doesn't mean it is in other species (and I believe responsiveness in cats develops with experience).

Response properties of superficial SC neurons is different from those found in mouse V1 neurons.

Response properties of superficial SC neurons are different in different animals.

Rucci et al. present an algorithm which performs auditory localization and combines auditory and visual localization in a common SC map. The mapping between the representations is learned using value-dependent learning.

SOMs and SOM-like algorithms have been used to model natural multi-sensory integration in the SC.

Anastasio and Patton model the deep SC using SOM learning.

Anastasio and Patton present a model of multi-sensory integration in the superior colliculus which takes into account modulation by uni-sensory projections from cortical areas.

In the model due to Anastasio and Patton, deep SC neurons combine cortical input multiplicatively with primary input.

Anastasio and Patton's model is trained in two steps:

First, connections from primary input to deep SC neurons are adapted in a SOM-like fashion.

Then, connections from uni-sensory, parietal inputs are trained, following an anti-Hebbian regime.

The latter phase ensures the principles of modality-matching and cross-modality.

Modulatory input from uni-sensory, parietal regions to SC follows the principles of modality-matching and cross-modality:

A deep SC neuron (generally) only receives modulatory input related to some modality if it also receives primary input from that modality.

Modulatory input related to some modality only affects responses to primary input from the other modalities.

Deactivating regions in AES or lateral suprasylvian cortex responsive to some modality can completely eliminate responses of deep SC neurons to that modality.

Wallace and Stein argue that some deep SC neurons receive input from some modalities only via cortex.

SOM learning produces clusters of neurons with similar modality responsiveness in the SC model due to Anastasio and Patton.

The model due to Anastasio and Patton reproduces multi-sensory enhancement.

Deactivating modulatory, cortical input also deactivates multi-sensory enhancement.

Saccades evoked by electric stimulation of the deep SC can be deviated towards the target of visual spatial attention. This is the case even if the task forbids a saccade towards the target of visual spatial attention.

Activation builds up in build-up neurons in the intermediate SC during the preparation of a saccade.

Activation build-up in build-up neurons is modulated by spatial attention.

Kustov's and Robinson's results support the hypothesis that there is a strong connection of action and attention.

Magosso et al. present a recurrent ANN model which replicates the ventriloquism effect and the ventriloquism aftereffect.

In Anastasio and Patton's SC model, the spatial organization of the SOM is not used to represent the spatial organization of the outside world, but to distribute different sensitivities to the input modalities in different neurons.

It's a bit strange that Anastasio and Patton's and Martin et al.'s SC models do not use the spatial organization of the SOM to represent the spatial organization of the outside world, but to distribute different sensitivities to the input modalities in different neurons.

KNN (or sparse coding) seems to be more appropriate for that.

Beck et al. model build-up in the SC as accumulation of evidence from sensory input.

Deactivating or stimulating certain parts of the the deeper layers of the SC induces arousal, freezing and escape behavior as well as a raise in blood pressure, heart rate, and respiration.

Reactions can be as complex as running and jumping.

Whether or not the complex aversive behavior patterns evoked by deactivating or stimulating certain brain regions is a direct effect of SC activity or rather the result of actual fear which in turn may be due to that specific SC activity is unclear.

LIP and the FEF are usually connected to decision making.

Fitting barn owls with prisms which induce a shift in where the owls see objects in their environment leads to a shift of the map of auditory space in the optic tectum.

The shift in the auditory space map in the optic tectum of owls whose visual perception was shifted by prisms is much stronger in juvenile than in mature owls.

Letting adult owls with shifted visual spatial perception hunt mice increases the amount by which the auditory space map in the owls' optic tectum is shifted (as compared to feeding them only dead mice).

Bergan et al. offer four factors which might explain the increase in shift of the auditory space maps in owls with shifted visual spatial perception:

  • Hunting represents a task in which accurate map alignment is important (owls which do not hunt presumably do not face such tasks),
  • more cross-modal experience (visual and auditory stimuli from the mice),
  • cross-modal experiences in phases of increased attention and arousal,
  • increased importance of accurate map alignment (important for feeding).

If increased importance of accurate map alignment is what causes stronger map alignment in the optic tectum of owls that hunt than in those of owls that do not hunt (with visually displacing prisms), then that could point either

  • to value-based learning in the OT
  • or to a role of cognitive input to the OT (hunting owls pay more attention/are more interested in audio-visual stimuli than resting or feeding owls).

According to Quaia, the Robinson model of saccade generation introduced the idea that saccades are controlled by a feedback loop in which the current eye position is compared to the target eye position and corrective motor signals are issued accordingly.

This idea was integrated in a family of later models.

After ablation of the SC, accurate saccades are still possible. Initially, trajectory and speed are impaired, but they recover.

Quaia et al. present a model of the saccadic system involving SC and cerebellum, which reproduces the fact that the ability to generate fast and precise saccades recovers after ablation of the SC.

There are multisensory neurons in the newborn macaque monkey's deep sc.

General sensory maps (and map register) are already present in the newborn macaque monkey's deep SC (though receptive fields are large).

Maturational state of the deep SC is different between species—particularly between altricial and precocial species.

The topographic map of visual space in the sSC is retinotopic.

The motor map of in the dSC is retinotopic.

The superior colliculus is retinotopically organized.

Activity in the SC drives the saccade burst generators which are oculomotor neurons in the reticular formation.

Tabareau et al. propose a scheme for a transformation from the topographic mapping in the SC to the temporal code of the saccadic burst generators.

According to their analysis, that code needs to be either linear or logarithmic.

Girard and Berthoz review saccade system models including models of the SC.

Except for two of the SC models, all focus on generation of saccades and do not consider sensory processing and in particular multisensory integration.

The receptive fields of multisensory neurons in the deep SC which are close to one another are highly correlated.

Both visual and auditory neurons in the deep SC usually prefer moving stimuli and are direction selective.

The range of directions deep SC neurons are selective for is usally wide.

A deep SC neuron which receives enough information from one modality to reliably determine whether a stimulus is in its receptive field does not improve its performance much by integrating information from another modality.

Patton et al. use this insight to explain the diversity of uni-sensory and multisensory neurons in the deep SC.

Anastasio drop the strong probabilistic interpretation of SC neurons' firing patterns in their learning model.

Activity in the deep SC has been described as different regions competing for access to motor resources.

Rowland et al. present four SC models.

The first SC model presented by Rowland et al. is a single-neuron model in which sensory and cortical input is simply summed and passed through a sigmoid squashing function.

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.

The SC model presented by Cuppini et al. has a circular topology to prevent the border effect.

Rucci et al. model learning of audio-visual map alignment in the barn owl SC. In their model, projections from the retina to the SC are fixed (and visual RFs are therefore static) and connections from ICx are adapted through value-dependent learning.

It is interesting that Rucci et al. modeled map alignment in barn owls using value-based learning so long before value based learning was demonstrated in map alignment in barn owls.

Occluding one ear early in life shifts the map of auditory space with respect to the map of visual space in barn owls. Prolonged occlusion of one ear early in life leads to a permanent realignment of the auditory map with the visual map.

Various cortical regions project to the SC.

Mysore and Knudsen say that deep SC neurons respond to relative saliency of a stimulus, i.e. to the saliency of stimuli in their receptive fields compared to the saliency of stimuli outside their receptive fields.

DLPFC projects directly to the SC.

Many of the cortical areas projecting to the SC have been implicated with attention.

Spatial attention can enhance the activity of SC neurons whose receptive fields overlap the attended region

In some SC neurons, receptive fields are not in spatial register across modalities.

Receptive fields of SC neurons in different modalities tend to overlap.

Multisensory SC cell receptive fields are not well-delineated regions in space in which and only in which a stimulus evokes a stereotyped response. Instead, they can have a region, or multiple regions, where they respond vigorously and others, surrounding those `hot spots', which in which the response is less strong.

Rearing barn owls in darkness results in mis-alignment of auditory and visual receptive fields in the owls' optic tectum.

Rearing barn owls in darkness results in discontinuities in the map of auditory space of the owls' optic tectum.

Rearing animals in darkness can result in anomalous auditory maps in their superior colliculi.

The model of natural multisensory integration and localization is based on the leaky integrate-and-fire neuron model.

Rucci et al. explain audio-visual map registration and learning of orienting responses to audio-visual stimuli by what they call value-dependent learning: After each motor response, a modulatory system evaluated whether that response was good, bringing the target into the center of the visual field of the system, or bad. The learning rule used by the system was such that it strengthened connections between neurons from the different neural subpopulations of the network if they were highly correlated whenever the modulatory response was strong, and weakened otherwise.

Rucci et al.'s system comprises artificial neural populations modeling MSO (aka. the nucleus laminaris), the central nucleus of the inferior colliculus (ICc), the external nucleus of the inferior colliculus (ICx), the retina, and the superior colliculus (SC, aka. the optic tectum). The population modeling the SC is split into a sensory and a motor subpopulation.

In Rucci et al.'s system, the MSO is modeled by computing Fourier transforms for each of the auditory signals. The activity of the MSO neurons is then determined by their individual preferred frequency and ITD and computed directly from the Fourier-transformed data.

In Rucci et al.'s model, neural weights are updated between neural populations modeling

  • ICC and ICx
  • sensory and motor SC.

The superficial SC is modeled by Casey et al.'s system by two populations of center-on and center-off cells (whose receptive fields are modeled by a difference of Gaussians) and four populations of direction-sensitive cells.

The superior colliculus is connected, directly or indirectly, to most parts of the brain.

Some authors distinguish only superficial and deep superior colliculus.

(Retinal) visual input to the left SC mainly originates in the retina of the right eye and vice-versa.

Certain neurons in the deep SC emit bursts of activity before making a saccade.

It has long been known that stimulating the SC can elicit eye movements.

The size and direction of a saccade before which deep SC neurons show the greatest activity depends on where they are in the SC: Neurons in medial regions of the SC tend to prefer saccades going up, neurons in lateral regions of the SC tend to prefer saccades going down.

Long saccades are preceded by strong activity of rostral neurons, short saccades by activity of caudal neurons.

Deep SC neurons which have preferred saccades have these preferred saccades also in total darkness. They thus do not simply respond to the specific location of a visual stimulus.

Robinson reports two types of motor neurons in the deep SC: One type has strong activity just (~20 milliseconds) before the onset of a saccade. The other type has gradually increasing activity whose peak is, again, around 12-20 milliseconds before onset.

Currently, three types of saccade-related neurons are distinguished in the deep SC:

  • Burst- and build-up neurons on the one hand,
  • fixation neurons on the other.

Microstimulation of OT neurons in the barn owl can evoke pupil dilation.

Lesions of the tectospinal tract leads to deficits in motor responses, while lesions of brachium and parts of the tectothalamic system produce contralateral visual neglect.

Ablation of the SC leads to temporary blindness and deficits in visual following.

Sprague and Meikle Jr. propose that the SC is involved in visual attention.

Ablation of the superficial SC does not result in blindness or orienting deficiencies. Only when the deep SC is ablated do these deficiencies occur—a remarkable finding considering that the superficial SC is the main target of retinotectal projections.

Onset times of visually guided saccades have a bimodal distribution. The faster type of saccades are termed `express saccades'. Ablation of the SC but not of the FEF makes express saccades disappear.

"SC ablation permanently reduces fixation accuracy, saccade frequency, and saccade velocity."

Removing both SCs and both FEFs leads to permanent deficits:

  • a decrease in fixation accuracy,
  • a neglect of the peripheral visual field,
  • saccade frequency is decreased,
  • the range of saccadic eye movements is reduced.

Schiller et al. did not observe the visuospatial neglect and stark loss of oculomotor function as did Sprague and Meikle.

Brainstem premotor neurons producing the commands for eye movements are located in pons, medulla (horizontal movements), and the rostral midbrain (vertical movements).

The superficial SC projects retinotopically to LGN.

The same regions in LGN receiving projections from the superficial SC project to the cortex.

Superficial layers of the SC project to deep layers.

Both deep and superficial layers in left and right SC project to the corresponding layer in the contralateral SC.

The ventral lateral geniculate nucleus projects to the deep SC.

The response of neurons in the SC to a given stimulus decreases if that stimulus is presented constantly or repeatedly at a relatively slow rate (once every few seconds, up to a minute).

In cats, the SC has a size of about 4.5 mm to 4.7 mm from the posterior to the anterior end and 6.0 mm to 6.2 mm from the medial to the lateral end.

Some neurons in the dSC respond to an auditory stimulus with a single spike at its onset, some with sustained activity over the duration of the stimulus.

Middlebrooks and Knudsen report on sharply delineated auditory receptive fields in some neurons in the deep cat SC, in which there is an optimal region from which stimuli elicit a stronger response than in other places in the RF.

A minority of deep SC neurons are omnidirectional, responding to sounds anywhere, albeit with a defined best area.

There is a map of auditory space in the deep superior colliculus.

There is considerable variability in the sharpness of spatial tuning in the responses to auditory stimuli of deep SC neurons.

The visual and auditory maps in the deep SC are in spatial register.

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 map of visual space in the superficial SC of the mouse is in rough topographic register with the map formed by the tactile receptive fields of whiskers (and other body hairs) in deeper layers.

The superficial mouse SC is not responsive to auditory or tactile stimuli.

The receptive fields of certain neurons in the cat's deep SC shift when the eye position is changed. Thus, the map of auditory space in the deep SC is temporarily realigned to stay in register with the retinotopic map.

In an fMRI experiment, Schneider found that spatial attention and switching between modes of attention (attending to moving or to colored stimuli) strongly affected SC activation, but results for feature-based attention were inconclusive.

The fact that Schneider did not find conclusive evidence for modulation of neural responses by feature-based attention might be related to the fact that the superficial SC does not seem to receive color-based information and deep SC seems to receive color-based information only via visual cortex.

There are projections from visual cortex to SC.

There are projections from auditory cortex to SC (from anterior ectosylvian gyrus).

There are projections from motor and premotor cortex to SC.

There are projections from primary somatosensory cortex to SC.

Posterior parietal cortex projects to the deep SC.

Visually active neurons in FEF do not project to SC. Motion-related neurons in FEF project to SC.

There are ascending pathways from SC to the eye fields through talamic structures.

There is a disynaptic connection from SC to the dorsal stream visual cortex, probably through the pulvinar.

The neural response of an SC neuron to one stimulus can be made weaker in some neurons by another stimulus at a different position in space. This stimulus can be in the same or in a different modality (in multi-sensory neurons). This effect is called depression.

Kadunce et al. did not find within-modality visual suppression as often as within-modality auditory suppression.

Kadunce et al. found that suppressive regions were large and that depression varied depending on position of the concurrent stimulus within the suppressive region. Suppression was generally strongest when concurrent stimuli were on the ipsilateral side.

Kadunce et al. say that two identical stimuli played at different points in space might lead to a translocation of the perceived stimulus and thus to a translocation of the hill of activation in the SC.

Kadunce et al. found that two auditory stimuli placed at opposing the edges of a neuron's receptive field, in its suppressive zone, elicited some activity in the neuron (although less than they expected).

Kadunce et al. found cross-modality depression less often than within-modality depression.

Kadunce et al. found that for the majority of neurons in which a stimulus in one modality could lead to depression in another modality that depression was one-way: Stimuli in the second modality did not depress responses to stimuli in the first.

Kadunce et al. found that SC neurons are very inhomogeneous wrt. to presence and size of suppressive zones.

It has been found that stimulating supposed motor neurons in the SC enhances responses of v4 neurons with the same receptive field as the SC neurons.

Krauzlis et al. argue that SC deactivation should have changed neural responses in cortex if it regulated attention through visual cortex.

Krauzlis et al.'s argument that SC deactivation should have changed neural responses in cortex if it regulated attention through visual cortex is a bit weak considering that stimulating SC does change sensory representations in v4.

Krauzlis et al. argument that animals without a well-developed neocortex show signs of selective attention similar to humans and other higher animals shows that neocortex may not be necessary for attention seems more appropriate than that of lack of influence of collicular deactivation on cortical responses.

The SC is involved in generating gaze shifts and other orienting behaviors.

The SC localizes events.

The uni-sensory, multi-sensory and motor maps of the superior colliculus are in spatial register.

Cats, being an altricial species, are born with little to no capability of multi-sensory integration and develop first multi-sensory SC neurons, then neurons exhibiting multi-sensory integration on the neural level only after birth.

In the development of SC neurons, receptive fields are initially very large and shrink with experience.

SC receives tactile localization-related inputs from the trigeminal nucleus.

Multisensory experience is necessary to develop normal multisensory integration.

Multisensory integration in the SC is similar in anesthetized and alert animals (cats).

ICx projects to intermediate and deep layers of SC.

There appears to be plasticity wrt. the auditory space map in the SC.

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.

Different types of retinal ganglion cells project to different lamina in the zebrafish optic tectum.

The lamina a retinal ganglion cell projects to in the zebrafish optic tectum does not change in the fish's early development. This is in contrast with other animals.

However, the position within the lamina does change.

Sub-cortical structures (like the SC) have bearing on cortical functionality.

In the Sprague effect, removing (or deactivating) one visual cortex eliminates visually induced orienting behavior to stimuli in the contralateral hemifield.

Lesioning (or deactivating) the contralateral SC restores the orienting behavior.

``The heminanopia that follows unilateral removal of the cortex that mediates visual behavior cannot be explained simply in classical terms of interruption of the visual behavior cannot be explained simply in classical terms of interruption of the visual radiations that serve cortical function.
Explanation fo the deficit requires a broader point of view, namely, that visual attention and perception are mediated at both forebrain and midbrain levels, which interact in their control of visually guided behavior.''

(Sprague, 1966)

SC receives input and represents all sensory modalities used in phasic orienting: vision, audition, somesthesis (haptic), nociceptic, infrared, electoceptive, magnetic, and ecolocation.

The stratum zonale is the outermost, almost cell-free lamina of the SC.

The stratum griseum superficiale is the SC layer below the stratum zonale. It contains many small cells.

The stratum opticum is the innermost of the superficial SC layers, below the stratum griseum. It is dominated by fibers including retinal projections.

The stratum griseum intermediale is the outermost lamina of the deep SC.

The stratum album intermediale is the second-outermost lamina of the deep SC, below the stratum griseum intermediale.

The stratum griseum profundum is the third-outmost lamina of the deep SC, below the stratum album intermediale.

The stratum album profundum is the lowest lamina of the deep SC, below the stratum griseum profundum.

The stratum album profundum borders to the periaqueductal gray.

There are alternative nomenclatures for the layers of the deep sc.

The layers and internal connectivity of the optic tectum is similar but different from those of the mammalian SC.

The nucleus of the brachium of the inferior colliculus (nbic) projects to intermediate and deep layers of SC.

SC receives auditory localization-related inputs from the IC.

SC neurons respond faster to stimuli based on luminance contrasts than on color contrast.

Superficial SC neurons seem to have little to no access to color information.

Deep SC neurons do react to stimuli based on color contrast.

There is reason to believe that color information reaches the SC via cortical routes.

The squirrel SC measures a few centimeters across in either direction.

Xu et al. stress the point that in their cat rearing experiments, multisensory integration arises although there is no reward and no goal-directed behavior connected with the stimuli.

The fact that multi-sensory integration arises without reward connected to stimuli motivates unsupervised learning approaches to SC modeling.

The precise characteristics of multi-sensory integration were shown to be sensitive to their characteristics in the experienced real world during early life.

It is interesting that multisensory integration arises in cats in experiments in which there is no goal-directed behavior connected with the stimuli as that is somewhat in contradiction to the paradigm of embodied cognition.

Xu et al. raised two groups of cats in darkness and presented one with congruent and the other with random visual and auditory stimuli. They showed that SC neurons in cats from the concruent stimulus group developed multi-sensory characteristics while the other mostly did not.

In the experiment by Xu et al., SC neurons in cats that were raised with congruent audio-visual stimuli distinguished between disparate combined stimuli, even if these stimuli were both in the neurons' receptive fields. Xu et al. state that this is different in naturally reared cats.

In the the experiment by Xu et al., SC neurons in cats that were raised with congruent audio-visual stimuli had a preferred time difference between onset of visual and auditory stimuli of 0s whereas this is around 50-100ms in normal cats.

In the the experiment by Xu et al., SC neurons in cats reacted best to auditory and visual stimuli that resembled those they were raised with (small flashing spots, broadband noise bursts), however, they generalized and reacted similarly to other stimuli.

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.

In Anastasio et al.'s model of multi-sensory integration in the SC, an SC neuron is connected to one neuron from each modality whose spiking behavior is a (Poisson) probabilistic function of whether there is a target in that modality or not.

Their single SC neuron then computes the posterior probability of there being a target given its inputs (evidence) and the prior.

Under the assumption that neural noise is independent between neurons, Anastasio et al.'s approach can be extended by making each input neuron its own modality.

Bayesian integration becomes more complex, however, because receptive fields are not sharp. The formulae still hold, but the neurons cannot simply use Poisson statistics to integrate.

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

Stuphorn et al. found neurons in the monkey SC whose activity was dependent on the retinotopic position of the target in a reaching task, but not to the actual path taken in reaching.

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.

McHaffie et al. describe subcortical loops, and in particular loops involving the SC, through the basal ganglia.

McHaffie et al. speculate that loops through various subcortical loops might solve the selection problem, ie. the gating of competing inputs to shared resources.

In the SC, this means that the basal ganglia decide which of the brain structures involved in gaze shifts access to the eye motor circuitry.

Goldberg and Wurtz found that neurons in the superficial SC respond more vigorously to visual stimuli in their receptive field if the current task is to make a saccade to the stimuli.

Responses of superficial SC neurons do not depend solely to intrinsic stimulus properties.

There is a distinction between two different kinds of bats: megabats and microbats. Megabats differ in size (generally), but also in the organization of their visual system. In particular, their retinotectal projections are different: while all of the retinotectal projections in microbats are contralateral, retinotectal projections in megabats are divided such that projections from the nasal part of the retina go to the ipsilateral SC and those from the peripheral part go to the contralateral SC. This is similar to primate vision.

In primates, retinotectal projections to each SC are such that each visual hemifield is mapped to one (contralateral) SC. This is in contrast with retinotectal projections in most other vertebrates, where all projections from one retina project to the contralateral SC.

Weber and Triesch's model learns task-relevant features.

However, a brain region like the SC, which serves a very general task, cannot specialize in one task—it has to serve all goals that the system has.

It therefore should change its behavior depending on the task. Attention is one mechanism which might determine how to change behavior in a given situation.

If the goal is predictive of the input, then a purely unsupervised algorithm could take a representation of the goal as just another input.

While it is possible that the goal often is predictive of the input, some error feedback is probably necessary to tune the degree to which the algorithm can be `distracted' by task-irrelevant but interesting stimuli.

Saeb et al. extend their model by a short-term memory which encodes the last action. This action memory is used to make up for noise and missing information.

SC is connected to motor plants via brainstem.

Lesions to SC and FEF individually do not eliminate saccades. Lesions to both do eliminate saccades.

The contribution of head-saccades to full saccades can be influenced by knowledge about the target of the next saccade.

Fujita models saccade suppression of endpoint variability by the cerebellum using their supervised ANN model for learning a continuous function of the integral of an input time series.

He assumes that the input activity originates from the SC and that the correction signal is supplied by sensory feedback.

In his model, Fujita abstracts away from the population coding present in the multi-sensory/motor layers of the SC.

Optic tectum and superior colliculus are homologues.

The tectum includes both sc (optic tectum) and ic

The SC is multisensory: it reacts to visual, auditory, and somatosensory stimuli. It does not only initiate gaze shifts, but also other motor behaviour.

The SC is involved in the transformation of multisensory signals into motor commands.

The SC maturates fast compared to the cortex; this is important to protect the young animal from threats in early life.

The mammalian SC is divided into seven layers with alternating fibrous and cellular layers.

The superficial layers include layers I-III, while the deep layers are layers IV-VII.

Some authors distinguish a third, intermediate, set of layers (IV,V).

There are ascending projections from the superficial SC to the Thalamus and from there to extrastriate cortex.

There are descending projections from the SC to the parabigeminal nucleus, or nucleus isthmii as it is called in non-mammals.

The deeper levels of SC are the targets of projections from cortex, auditory, somatosensory and motor systems in the brain.

The superficial SC is visuotopic.

The part of the visual map in the superficial SC corresponding to the center of the visual field has the highest spatial resolution.

Visual receptive fields in the deeper SC are larger than in the superficial SC.

The parts of the sensory map in the deeper SC corresponding to peripheral visual space have better representation than in the visual superficial SC.

Do the parts of the sensory map in the deeper SC corresponding to peripheral visual space have better representation than in the visual superficial SC because they integrate more information; does auditory or tactile localization play a more important part in multisensory localization there?

Neurons in the deep SC whose activity spikes before a saccade have preferred amplitudes and directions: Each of these neurons spikes strongest before a saccade with these properties and less strongly before different saccades.

Moving the eyes shifts the auditory and somatosensory maps in the SC.

(Some) SC neurons in the newborn cat are sensitive to tactile stimuli at birth, to auditory stimuli a few days postnatally, and to visual stimuli last.

Visual responsiveness develops in the cat first from top to bottom in the superficial layers, then, after a long pause, from top to bottom in the lower layers.

The basic topography of retinotectal projections is set up by chemical markers. This topography is coarse and is refined through activity-dependent development.

We do not know whether other sensory maps than the visual map in the SC are initially set up through chemical markers, but it is likely.

If deep SC neurons are sensitive to tactile stimuli before there are any visually sensitive neurons, then it makes sense that their retinotopic organization be guided by chemical markers.

There's a retinotopic, polysynaptic pathway from the SC through LGN.

There are at least polysynaptic pathways from deep SC to cortex.

Polysynaptic pathways from deep SC to cortex may explain facilitation of visual processing in the V1 caused by SC

Kao et al. did not find visually responsive neurons in the deep layers of the cat SC within the first three postnatal weeks.

Overt visual function can be observed in developing kittens at the same time or before visually responsive neurons can first be found in the deep SC.

Some animals are born with deep-SC neurons responsive to more than one modality.

However, these neurons don't integrate according to Stein's single-neuron definition of multisensory integration. This kind of multisensory integration develops with experience with cross-modal stimuli.

Less is known about the motor properties of SC neurons than about the sensory properties.

Electrical stimulation of the cat SC elicits eye and body movements long before auditory or visual stimuli could have that effect.

These movements already follow the topographic organization of the SC at least roughly.

Maybe attention controls whether or not multi-sensory integration (MSI) happens at all (at least in SC)? That would be in line with findings that without input from AES and rLS, there's no MSI.

Are AES and rLS cat homologues to the regions cited by Santangelo and Macalluso as regions responsible for auditory and visual attention?

Multi-sensory neurons in the SC are only in the intermediate and deep layers.

The most important cortical input to the SC (in cats) comes from layer V cortical neurons from a number of sub-regions of the anterior ectosylvian sulcus (AES):

  • anterior ectosylvian visual area (AEV)
  • the auditory field of AES (FAES)
  • and the fourth somatosensory area (SIV)

These populations in themselves are uni-sensory.

Neurons that receive auditory and visual ascending input also receive (only) auditory and visual descending projections.

Most multisensory SC neurons project to brainstem and spinal chord.

There are monosynaptic excitatory AES-SC projections and McHaffie et al. state that "the predominant effect of AES on SC multisensory neurons is excitatory."

The optic tectum (OT) receives information on sound source localization from ICx.

Hyde and Knudsen found that there is a point-to-point projection from OT to IC.

A faithful model of the SC should probably adapt the mapping of auditory space in the SC and in another model representing ICx.

Mammals seem to have SC-IC connectivity analogous to that of the barn owl.

Hyde and Knudsen elaborate on which neurons in the owl optic tectum (OT) project where.

Hyde and Knudsen propose that the OT-IC projection conveys what they call a "template-based instructive signal" which aligns the auditory space map in ICx with the retinotopic space map in SC.

Colonius and Diederich argue that deep-SC neurons spiking behavior can be interpreted as a vote for a target rather than a non-target being in their receptive field.

This is similar to Anastasio et al.'s previous approach.

There are a number of problems with Colonius' and Diederich's idea that deep-SC neurons' binary spiking behavior can be interpreted as a vote for a target rather than a non-target being in their RF. First, these neurons' RFs can be very broad, and the strength of their response is a function of how far away the stimulus is from the center of their RFs. Second, the response strength is also a function of stimulus strength. It needs some arguing, but to me it seems more likely that the response encodes the probability of a stimulus being in the center of the RF.

Colonius and Diederich argue that, given their Bayesian, normative model of neurons' response behavior, neurons responding to only one sensory modality outperform neurons responding to multiple sensory modalities.

Colonius' and Diederich's explanation for uni-sensory neurons in the deep SC has a few weaknesses: First, they model the input spiking activity for both the target and the non-target case as Poisson distributed. This is a problem, because the input spiking activity is really a function of the target distance from the center of the RF. Second, they explicitly model the probability of the visibility of a target to be independent of the probability of its audibility.

If SC neurons spiking behavior can be interpreted as a vote for a target rather than a non-target being in their receptive field, then the decisions must be made somewhere else because they then do not take into account utility.

The deeper levels of SC receive virtually no primary visual input (in cats and ferrets).

Visual receptive fields in the superficial monkey SC do vary substantially in RF size with RF eccentricity.

In some animals, receptive field sizes do and in some they don't change substantially with RF excentricity.

The neurons in the superficial (rhesus) monkey SC do not exhibit strong selectivity for specific shapes, stimulus orientation, or moving directions. Some of them do show selectivity to stimuli of specific sizes.

The activity profiles for stimuli moving through superficial SC neuron RFs shown in Cynader and Berman's work look similar to Poisson-noisy Gaussians, however, the authors state that the strength of a response to a stimulus was the same regardless where in the activating region it was shown.

The neurons in the superficial (rhesus) monkey SC largely prefer moving stimuli over non-moving stimuli.

In the intermediate layers of the monkey SC, neurons have a tendency to reduce or otherwise their reaction to presentations of the same stimulus over time.

There are marked differences in the receptive field properties of superficial cat and monkey SC neurons.

Ocular dominance stripes have been shown to exist in the monkey SC. In some places, they weren't crisp but ran into each other.

Pavlou and Casey model the SC.

They use Hebbian, competitive learning to learn and topographic mapping between modalities.

They also simulate cortical input.

Martin et al. model multisensory integration in the SC using a SOM algorithm.

Input in Martin et al.'s model of multisensory integration in the SC is an $m$-dimensional vector for every data point, where $m$ is the number of modalities. Data points are uni-modal, bi-modal, or tri-modal. Each dimension of the data point codes stochastically for the combination of modalities of the data point. The SOM learns to map different modality combinations to different regions into its two-dimensional grid.

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

Bell et al. found that playing a sound before a visual target stimulus did not increase activity in the neurons they monitored for long enough to lead to (neuron-level) multisensory integration.

Bell et al. make it sound like enhancement in SC neurons due to exogenous, visual, spatial attention is due to residual cue-related activity which is combined (non-linearly) with target-related activity.

If enhancement in SC neurons due to exogenous, visual, spatial attention is due to residual cue-related activity which is combined (non-linearly) with target-related activity, then that casts an interesting light on (the lack of) intra-modal enhancement:

The only difference between an intra-modal cue-stimulus combination and an intra-modal stimulus-stimulus combination lies in the temporal order of the two. Therefore, if two visual stimuli presented in the receptive field of an SC at the same time) neuron do not enhance the response to each other, then the reason can only be a matter of timing.

In an fMRI experiment, Fairhall and Macaluso found that attending (endogenously, spatially) to congruent audio-visual stimuli (moving lips and speech) produced greater activation in SC than either attending to non-congruent stimuli or not attending to congruent stimuli.

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).

Different regions project to different lamina of the SC.

An SC output neuron which projects to some structure outside the SC may sample input from SC lamina according to the requirements of the target of its projections.

There are monosynaptic connections from the retina to neurons both in the superficial and deeper layers of the SC.

In the study due to Xu et al., multi-sensory enhancement in specially-raised cats decreased gradually with distance between uni-sensory stimuli instead of occurring if and only if stimuli were present in their RFs. This is different from cats that are raised normally in which enhancement occurs regardless of stimulus distance if both uni-sensory components both are within their RF.

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.

Visual receptive fields in the sc usually consist of an excitatory central region and an inhibitory surround.

(Auditory receptive fields also often seem to show this antagonism.)

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

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

The superior colliculus receives input from various sensory brain areas. According to King, these inputs are uni-sensory, as far as we know.

According to King, the principal function of the SC is initiating gaze shifts.

The SC also seems to be involved in reaching and other forelimb-related motor tasks and has been associated with complex vision-guided arm-gestures in humans.

Enhancement in the SC happens only between stimuli from different modalities.

Depression in the SC happens between stimuli from the same modality.

Is there really no enhancement between different cues from the same modalities, like eg. contrast and color?

Patton and Anastasio present a model of "enhancement and modality-specific suppression in multi-sensory neurons" that requires no multiplicative interaction. It is a follow-up of their earlier functional model of these neurons which requires complex computation.

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.

Anastasio et al.'s model of SC neurons assumes that these neurons receive multiple inputs with Poisson noise and apply Bayes' rule to calculate the posterior probability of a stimulus being in their receptive fields.

Anastasio et al. point out that, given their model of SC neurons computing the probability of a stimulus being in their RF with Poisson-noised input, a sigmoid response function arises for uni-sensory input.

Without an intact association cortex (or LIP), SC neurons cannot develop or maintain cross-modal integration.

(Neither multi-sensory enhancement nor depression.)

There is no depression in the immature SC.

Cuppini et al. model SC receptive fields (actually: spatial tuning curves) as continuous functions of the distance of a stimulus from the center of the RF.

In Anastasio et al.'s work, receptive fields are binary: either a stimulus is in the field or it isn't; if the average response of an SC neuron is smaller for stimuli that are further away from the center of the RF is smaller, then that's because inference there is less effective.

My explanation for different responsiveness to the individual modalities in SC neurons: They do causal inference/model selection. different neurons coding for the same point in space specialize in different stimulus (strength) combinations.

This is basically, what Anastasio and Patton's model does (except that it does not seem to make sense to me that they use the SOM's spatial organization to represent different sensory combinations).

SC has been implicated as part of a subcortical visual pathway which may drive face detection and orienting towards faces in newborns.

The subcortical visual pathway which may drive face detection and orienting towards faces in newborns hypothesized by Johnson also includes amygdala and pulvinar.

According to the hypothesis expressed by Johnson, amygdala, pulvinar, and SC together form a sub-cortical pathway which detects faces, initiates orienting movements towards faces, and activates cortical regions.

This implies that this pathway may be important for the development of the `social brain', as Johnson puts it.

Most of the multi-sensory neurons in the (cat) SC are audio-visual followed by visual-somatosensory, but all other combinations can be found.

One reason for specifically studying multi-sensory integration in the (cat) SC is that there is a well-understood connection between input stimuli and overt behavior.

The SC is also involved in eye, head, whole-body, ear, whisker and other body movements.

What we find in the SC we can use as a guide when studying other multi-sensory brain regions.

Multisensory stimuli can be integrated within a certain time window; auditory or somatosensory stimuli can be integrated with visual stimuli even though they arrive delayed wrt. visual stimuli.

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

Descending inputs from association cortex to SC are uni-sensory.

AES integrates audio-visual inputs similar to SC.

AES has multisensory neurons, but they do not project to SC.

Map alignment in the SC is expensive, but it pays off because it allows for a single interface between sensory processing and motor output generation.

There are modulatory projections from AES to SC. This looks like a parallel to connections in visual cortex, because

  • SC is "low" in the information processing hierarchy and AES is high,
  • projections from SC are topographically organized
  • AES-SC-projections are modulatory.

However,

  • I don't know about the topographic organization and bifurcation properties of the descending projection.
  • I don't know if there are (indirect?) connections from SC to AES and whether they are topographically organized

The idea that neural activity does not primarily represent the world but 'action pointers', as put by Engel et al., speaks to the deep SC which is both 'multi-modal' and 'motor'.

If there is a close connection between the state of the world and the required actions, then it is easy to confuse internal representations of the world with `action pointers'.

The idea that the SC should learn to move the eyes such that it sees something interesting afterwards is in line with the idea that the brain should represent action pointers instead of actions.

Benevenuto and Fallon found projections from the SC mostly to midbrain and thalamus structures. They did not study projections to cortical regions. In detail, they found projections to:

Midbrain:

  • inferior colliculus
  • pretectum

Thalamus:

  • ventral lgn
  • dorsal lgn
  • suprageniculate nucleus
  • intralaminar nuclei
  • parafascicular nucleus
  • parts of dorsomedial nucleus
  • suprageniculate nucleus
  • certain pulvinar nuclei
  • lateral posterior nucleus
  • reunions nucleus
  • ventral posterior inferior nucleus
  • ventral posterior lateral nuclei
  • ventral lateral nucleus
  • limitans nucleus

Hypothalamus

  • dorsomedial nucleus

Other

  • Fields of Forel (subthalamic)
  • zona incerta
  • accessory optic tract (in midbrain)

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.

The ANN model of multi-sensory integration in the SC due to Ohshiro et al. uses divisive normalization to model multisensory integration in the SC.

Neurons in the superficial SC are almost exclusively visual in most species.

The auditory field of the anterior ectosylvian sulcus (fAES) has strong corticotectal projections (in cats).

Some cortical areas are involved in orienting towards auditory stimuli:

  • primary auditory cortex (A1)
  • posterior auditory field (PAF)
  • dorsal zone of auditory cortex (DZ)
  • auditory field of the anterior ectosylvian sulcus (fAES)

Only fAES has strong cortico-tectal projections.

One family of models for saccades and anti-saccades are the `accumulator models'.

These models pose that activation of saccade and saccade suppression neurons race each other. The one first to reach a threshold wins.

Activation of FEF and SC neurons is higher before direction error saccades in anti-saccade tasks than before correct anti-saccades.

Munoz and Everling assume that there are distinct populations of fixation and saccade neurons in the SC and FEF.

In a more recent paper, Casteau and Vitu state that there is some debate about that. However, they, too argue for distinct fixation neurons. On the other hand, they also state that fixation neurons probably are not located in the SC itself, which is in contrast of what Munoz and Everling write.

Rowland et al. derive a model of cortico-collicular multi-sensory integration from findings concerning the influence of deactivation or ablesion of cortical regions anterior ectosylvian cortex (AES) and rostral lateral suprasylvian cortex.

Rowland et al. derive a model of cortico-collicular multi-sensory integration from findings concerning the influence of deactivation or ablesion of cortical regions anterior ectosylvian cortex (AES) and rostral lateral suprasylvian cortex.

It is a single-neuron model.

Morén et al. present a spiking model of SC.

Cuppini et al. expand on their earlier work in modeling cortico-tectal multi-sensory integration.

They present a model which shows how receptive fields and multi-sensory integration can arise through experience.

Trappenberg presents a competitive spiking neural network for generating motor output of the SC.

Ghahramani et al. discuss computational models of sensorimotor integration.

Need to look at models of multi-sensory integration as well; they are not necessarily models of the SC, but relevant.

There seems to be an ascending pathway from superficial SC to the medial temporal area (MT) through the pulvinar nuclei (inferior pulvinar).

Berman and Wurtz found neurons in the pulvinar nuclei which received input from SC and sent output to MT.

Pulvinar neurons project to the SC.

Marr speaks of vision as one process, whose task is to generate `a useful description of the world'. However, there is more than one actual goal of vision (though they share similar properties) and thus there are different representations and algorithms being used in the different parts of the brain concerned with these goals.

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.

The direction of a saccade is population-coded in the SC.

There exist two hypotheses for how saccade trajectory is population-coded in the SC:

  • the sum of the contributions of all neurons
  • the weighted average of contributions of all neurons

The difference is in whether or not the population response is normalized.

According to Lee et al., the vector summation hypothesis predicts that any deactivation of motor neurons should result in hypometric saccades because their contribution is missing.

According to the weighted average hypothesis, the error depends on where the saccade target is wrt. the preferred direction of the deactivated neurons.

Lee et al. found that de-activation of SC motor neurons did not always lead to hypometric saccades. Instead, saccades where generally too far from the preferred direction of the de-activated neurons. They counted this as supporting the vector averaging hypothesis.

Anastasio et al. have come up with a Bayesian interpretation of neural responses to multi-sensory stimuli in the SC. According to their view, enhancement, depression and inverse effectiveness phenomena are due to neurons integrating uncertain information from different sensory modalities.

The superior colliculus sends motor commands to cerebellum and reticular formation in the brainstem.

Lateral intraparietal area (LIP) projects to intermediate layers of SC.

The retina projects to the superficial SC directly.

the frontal eye fields (fef) project to the SC and to the brainstem directly.

there are wide-field and narrow-field receptive field cells in the superficial sc

Substantia nigra pars reticulata (SNpr) tonically inhibits SC

Some models assume SC output encodes saccade amplitude and direction. In other models, each spike from a burst neuron encodes a motion segment, with length and direction depending on the position of the neuron and strength of connection to brainstem areas

Connectivity-wise, the strongest connections between SC and cortex are between SC and parietal lobe.

Rucci et al. model multi-sensory integration in the barn owl OT using leaky integrator firing-rate neurons and reinforcement learning.

Rucci et al. test their model of multi-sensory integration in the barn owl OT in a robot.

Rucci et al. suggest that high saliency in the center of the visual field can act as a reward signal for pre-saccadic neural activation.

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.

Pitti et al. claim that their model explains preference for face-like visual stimuli and that their model can help explain imitation in newborns. According to their model, the SC would develop face detection through somato-visual integration.

Pitti et al.'s claim of predicting with their model face-detectors in the developing SC and explaining preference for visual stimuli is problematic.

First, it is implausible that the somato-visual map created through multi-sensory learning would map the location at which a child sees facial features to the same neurons that respond to corresponding features in the child's own face. A child does not see a mouth where it sees a ball or hand touching its own mouth.

Secondly, their paper at least does not explain why their model should develop preference for points that correspond with their own facial features. The only reason that this could be happening I see is that their grid model of the child's face has a higher density of nodes around eyes, nose, and mouth, such that those regions are denser in the resulting map. But this would not explain why the configuration of features corresponding to a face would have higher saliency.