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A computational perspective on the neural basis of multisensory spatial representations

Abstract

We argue that current theories of multisensory representations are inconsistent with the existence of a large proportion of multimodal neurons with gain fields and partially shifting receptive fields. Moreover, these theories do not fully resolve the recoding and statistical issues involved in multisensory integration. An alternative theory, which we have recently developed and review here, has important implications for the idea of 'frame of reference' in neural spatial representations. This theory is based on a neural architecture that combines basis functions and attractor dynamics. Basis function units are used to solve the recoding problem, whereas attractor dynamics are used for optimal statistical inferences. This architecture accounts for gain fields and partially shifting receptive fields, which emerge naturally as a result of the network connectivity and dynamics.

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Figure 1: A schematic representation of the standard theory for multisensory spatial integration and sensorimotor transformations.
Figure 2: A neural network for coordinate transformations using basis functions.
Figure 3: A recurrent basis function layer with attractor dynamics.
Figure 4: A partially shifting receptive field.
Figure 5: A schematic representation of a basis function network for reaching towards visual, auditory and tactile targets.

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Correspondence to Alexandre Pouget.

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MIT Encyclopedia of Cognitive Sciences

multisensory integration

spatial perception

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Pouget, A., Deneve, S. & Duhamel, JR. A computational perspective on the neural basis of multisensory spatial representations. Nat Rev Neurosci 3, 741–747 (2002). https://doi.org/10.1038/nrn914

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