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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.

Patients with lesions in V1 or striate were found to still be able to discriminate gender and expression of faces.

Miikulainen et al. use their SOM-based algorithms to model the visual cortex.

Miikulainen et al. use a hierarchical version of their SOM-based algorithm to model natural development of visual capabilities.

LGN and V4 have distinct layers for each eye.

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

Activity of the SC affects activity in cortical regions.

Spatial visual attention increases the activity of neurons in the visual cortex whose receptive fields overlap the attended region.

Feature-based visual attention increases the activity of neurons in the visual cortex which respond to the attended feature.

Spatial and feature-based visual attention are additive: together, they particularly enhance the activity of any neuron whose receptive field encompasses the attended region, contains a stimulus with the attended feature, and prefers that feature.

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

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.

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

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

Multisensory integration in cortex has been studied less than in the midbrain, but there is work on that.

Butts and Goldman use Gaussian functions to model the receptive fields of V1 neurons.

Various cortical regions project to the SC.

Neurons at later stages in the hierarchy of visual processing extract very complex features (like faces).

DLPFC projects directly to the SC.

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

fAES is not tonotopic. Instead, its neurons are responsive to spatial features of sounds. No spatial map has been found in fAES (until at least 2004).

AES has been implicated with selective attention.

Receptive fields in AEV tend to be smaller for cells with RF centers at the center of the visual field than for those with RF centers in the periphery.

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

Primary somatosensory cortex is somatotopic.

Posterior parietal cortex projects to the deep SC.

SEF projects directly to the SC, but different researchers disagree on the SC layers the projections terminate.

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

The frontoparietal network seems involved in executive control and orienting.

The anterior cingulate cortex is likely involved with regulating attention.

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

Koulakov and Chklovskii assume that sensory neurons in cortex preferentially connect to other neurons whose feature-preferences do not differ more than a certain amount from their own feature-preferences. Further, they argue that long connections between neurons incur a metabolic cost. From this, they derive the hypothesis that the patterns of feature selectivity seen in neural populations are the result of minimizing the distance between similarly selective neurons.

Koulakov and Chklovsky show that various selectivity patterns emerge from their theorized cost minimization, given different parameterizations of preference for connections to similarly-tuned neurons.

Weber presents a continuous Hopfield-like RNN as a model of complex cells in V1. This model receives input from a sparse coding generative Helmholtz machine, described earlier as a model of simple cells in V1, and which produces topography by coactivating neighbors in its "sleep phase". The complex cell model with its horizontal connections is trained to predict the simple cells' activations, while input images undergo small random shifts. The trained network features realistic centre-surround weight profiles (in position- and orientation-space) and sharpened orientation tuning curves.

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.

Removing large parts of cortex—even a full hemisphere—does not result in a loss of consciousness.

Absence epilepsy—sudden loss of consciousness with amnesia but not always with total loss of cognitive function—has been induced by electrostimulation of the upper brainstem, but not of cortical regions.

Cortical structures do not always control our overt behavior. Instead, sub-cortical areas sometimes override cortical tendencies.

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

Cortex uses the actuators through the midbrain and basal diencephalon. Since the midbrain is much smaller than the neocortex in humans, it acts as a bottleneck which integrates, serializes, and coordinates overt behavior.

As integrated, serial, and coordinated behavior is a feature of consciousness, it makes sense that the midbrain may play an important part in making consciousness.

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

Classical models assume that learning in cortical regions is well described in an unsupervised learning framework while learning in the basal ganglia can be modeled by reinforcement learning.

Representations in the cortex (eg. V1) develop differently depending on the task. This suggests that some sort of feedback signal might be involved and learning in the cortex is not purely unsupervised.

Some task-dependency in representations may arise from embodied learning where actions bias experiences being learned from.

Conversely, the narrow range of disparities reflected in disparaty-selective cells in visual cortex neurons might be due to goal-directed feature learning.

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

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

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

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

When asked to ignore stimuli in the visual modality and attend to the auditory modality, increased activity in the auditory temporal cortex and decreased activity in the visual occipital cortex can be observed (and vice versa).

With two stimuli in the receptive field, one with features of a visual search target and one with different features

  • increases average neural activity in cortex compared to the same two objects without attending to any features
  • decreases average neural activity if spatial attention is on the location of the non-target compared to when it is on the target.

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.

Divisive normalization models describe neural responses well in cases of

  • olfactory perception in drosophila,
  • visual processing in retina and V1,
  • possibly in other cortical areas,
  • modulation of responses through attention in visual cortex.

Visual cortex is not fully developed at birth in primates.

The fact that visual cortex is not fully developed at birth, but newborn children prefer face-like visual stimuli to other visual stimuli could be explained by the presence of a subcortical face-detector.

The fact that visual cortex is not fully developed at birth, but newborn children prefer face-like visual stimuli to other visual stimuli could be explained by the presence of a subcortical face-detector.

Looking behavior in newborns may be dominated by non-cortical processes.

The visual cortex is hierarchically organized.

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).

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

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

There is multisensory integration in areas typically considered unisensory, eg. primary and secondary auditory cortex.

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