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Maps of sensory space in different sensory modalities can, if brought into register, give rise to an amodal representation of space.

If sensory maps of uni-modal space are brought into register, then cues from different modalities can access shared maps of motor space.

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.

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.

The motor map of in the dSC is retinotopic.

The superior colliculus is retinotopically organized.

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.

Electrostimulation of putamen neurons can evoke body movement consistent with the map of somatosensory space in that brain region.

The motor map is not monotonic across the entire FEF, but sites that are close to each other have similar characteristic saccades.

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 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.

Yan et al. present a system which uses auditory and visual information to learn an audio-motor map (in a functional sense) and orient a robot towards a speaker. Learning is online.

According to Wilson and Bednar, there are four main families of theories concerning topological feature maps:

• input-driven self-organization,
• minimal-wire length,
• place-coding theory,
• Turing pattern formation.

Wilson and Bednar argue that input-driven self-organization and turing pattern formation explain how topological maps may arise from useful processes, but they do not explain why topological maps are useful in themselves.

According to Wilson and Bednar, wire-length optimization presupposes that neurons need input from other neurons with similar feature selectivity. Under that assumption, wire length is minimized if neurons with similar selectivities are close to each other. Thus, the kind of continuous topological feature maps we see optimize wire length.

The idea that neurons should especially require input from other neurons with similar spatial receptive fields is unproblematic. However, Wilson and Bednar argue that it is unclear why neurons should especially require input from neurons with similar non-spatial feature preferences (like orientation, spatial frequency, smell, etc.).

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.

Sensory maps are not required to ensure coverage of the sensory spectrum.

Pooling the activity of a set of similarly-tuned neurons is useful for increasing the sharpness of tuning. A neuron which pools from a set of similarly-tuned neurons would have to make shorter connections if these neurons are close together. Thus, there is a reason why it can be useful to connect preferentially to a set of similarly-tuned neurons. This reason might be part of the reason behind topographic maps.

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

Kohonen states that early SOMs were meant to model brain maps and how they come to be.

Topographic mapping is pervasive throughout sensory-motor processing.

Some sensory-motor maps are complex: they are not a simple spatiotopic mapping, but comprise internally spatiotopic neighborhoods' which, on a much greater scale are organized spatiotopically, but across which the same point in space may be represented redundantly.

The motmap algorithm uses reinforcement learning to organize behavior in a two-dimensional map.

The complex structure of sensory-motor maps may be due to a mapping from a high-dimensional manifold into a two-dimensional space. This kind of map would also occur in Ring's motmaps.

Topographic maps keep information within spatial context and spatial context is often important for computations such as comparison between inputs from near-by locations.

Topographic maps can help minimize wire length in neural networks.