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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.

Rucci et al.'s neural network learns how to align ICx and SC (OT) maps by means of value-dependent learning: The value signal depends on whether the target was in the fovea after a saccade.

Rucci et al.'s model of learning to combine ICx and SC maps does not take into account the point-to-point projections from SC to ICx reported later by Knudsen et al.

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).

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.

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.