Computing multisensory target probabilities on a neural map

• Main Authors: Anastasio, T.J., Patton, P.E.
• Format: Conference Proceeding
• Language: English
• Subjects: Circuits; Computational modeling; Information analysis; Neurons; Neuroscience; Optical sensors; Physiology; Random variables; Sensor fusion; Spinal cord
• ISSN: 1094-687X; 1558-4615
• DOI: 10.1109/IEMBS.2001.1019068

The superior colliculus is organized topographically as a neural map. The deep layers of the colliculus detect and localize targets in the environment by integrating input from multiple sensory systems. Some deep colliculus neurons receive input of only one sensory modality (unimodal) while others receive input of multiple modalities. Multimodal deep SC neurons exhibit multisensory enhancement, in which the response to input of one modality is augmented by input of another modality. Multisensory enhancement is magnitude dependent in that combinations of smaller single-modality responses produce larger amounts of enhancement. These findings are consistent with the hypothesis that deep colliculus neurons use sensory input to compute the probability that a target has appeared at their corresponding location in the environment. Multisensory enhancement and its magnitude dependence can be simulated using a model in which sensory inputs are random variables and target probability is computed using Bayes' Rule. Informational analysis of the model indicates that input of another modality can indeed increase the amount of target information received by a multimodal neuron, but only if input of the initial modality is ambiguous. Unimodal deep colliculus neurons may receive unambiguous input of one modality and have no need of input of another modality.