Show Reference: "Using Bayes' Rule to Model Multisensory Enhancement in the Superior Solliculus"

Using Bayes' Rule to Model Multisensory Enhancement in the Superior Solliculus Neural Computation, Vol. 12, No. 5. (May 2000), pp. 1165-1187, doi:10.1162/089976600300015547 by Thomas J. Anastasio, Paul Patton, Kamel Belkacem-Boussaid
    abstract = {The deep layers of the superior colliculus ({SC}) integrate multisensory inputs and initiate an orienting response toward the source of stimulation (target). Multisensory enhancement, which occurs in the deep {SC}, is the augmentation of a neural response to sensory input of one modality by input of another modality. Multisensory enhancement appears to underlie the behavioral observation that an animal is more likely to orient toward weak stimuli if a stimulus of one modality is paired with a stimulus of another modality. Yet not all deep {SC} neurons are multisensory. Those that are exhibit the property of inverse effectiveness: combinations of weaker unimodal responses produce larger amounts of enhancement. We show that these neurophysiological findings support the hypothesis that deep {SC} neurons use their sensory inputs to compute the probability that a target is present. We model multimodal sensory inputs to the deep {SC} as random variables and cast the computation function in terms of Bayes' rule. Our analysis suggests that multisensory deep {SC} neurons are those that combine unimodal inputs that would be more uncertain by themselves. It also suggests that inverse effectiveness results because the increase in target probability due to the integration of multisensory inputs is larger when the unimodal responses are weaker.},
    address = {Beckman Institute and Department of Molecular and Integrative Physiology, University of Illinois at Urbana/Champaign, 61801, USA.},
    author = {Anastasio, Thomas J. and Patton, Paul and Belkacem-Boussaid, Kamel},
    citeulike-article-id = {403897},
    citeulike-linkout-0 = {},
    citeulike-linkout-1 = {},
    citeulike-linkout-2 = {},
    citeulike-linkout-3 = {},
    doi = {10.1162/089976600300015547},
    issn = {0899-7667},
    journal = {Neural Computation},
    keywords = {ann, bayes, enhancement, model, multi-modality, probability, sc, suppression},
    month = may,
    number = {5},
    pages = {1165--1187},
    pmid = {10905812},
    posted-at = {2012-05-04 17:40:13},
    priority = {2},
    title = {Using {B}ayes' Rule to Model Multisensory Enhancement in the Superior Solliculus},
    url = {},
    volume = {12},
    year = {2000}

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A deep SC neuron which receives enough information from one modality to reliably determine whether a stimulus is in its receptive field does not improve its performance much by integrating information from another modality.

Patton et al. use this insight to explain the diversity of uni-sensory and multisensory neurons in the deep SC.

In Anastasio et al.'s model of multi-sensory integration in the SC, an SC neuron is connected to one neuron from each modality whose spiking behavior is a (Poisson) probabilistic function of whether there is a target in that modality or not.

Their single SC neuron then computes the posterior probability of there being a target given its inputs (evidence) and the prior.

In Anastasio et al. use their model to explain enhancement and the principle of inverse effectiveness.

Colonius and Diederich argue that deep-SC neurons spiking behavior can be interpreted as a vote for a target rather than a non-target being in their receptive field.

This is similar to Anastasio et al.'s previous approach.

Anastasio et al. present a model of the response properties of multi-sensory SC neurons which explains enhancement, depression, and super-addititvity using Bayes' rule: If one assumes that a neuron integrates its input to infer the posterior probability of a stimulus source being present in its receptive field, then these effects arise naturally.

Anastasio et al.'s model of SC neurons assumes that these neurons receive multiple inputs with Poisson noise and apply Bayes' rule to calculate the posterior probability of a stimulus being in their receptive fields.

Anastasio et al. point out that, given their model of SC neurons computing the probability of a stimulus being in their RF with Poisson-noised input, a sigmoid response function arises for uni-sensory input.

Anastasio et al. have come up with a Bayesian interpretation of neural responses to multi-sensory stimuli in the SC. According to their view, enhancement, depression and inverse effectiveness phenomena are due to neurons integrating uncertain information from different sensory modalities.