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[Illinois]: Posterior probability of a target given input for two senses (Bayes')

By Lisa Sproat

University of Illinois at Urbana-Champaign

Computes the posterior probability of a target given sensory input of two modalities (i.e., visual and auditory)

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Version 1.0a - published on 19 Aug 2013

doi:10.4231/D3DZ0320N cite this

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Abstract

This tool computes the posterior probability of a target given sensory input of two modalities (i.e., visual and auditory).

FIGURE 9.11 depicts a single, sigmoidal unit used to simulate a bisensory neuron in the superior colliculus. The unit y receives inputs X1 and x2 and a bias b. The weight matrix V is a row vector containing the weights of the three connections to y from x1, x2, and b. The unit computes the weighted sum of its inputs and passes the result through the sigmoidal squashing function (see Chapter 6). Inputs x1 and x2 could represent visual and auditory inputs, respectively, and the bias could represent constant influences on the firing rate of the collicular neuron.

This tool is built from the MATLAB scripts for Tutorial on Neural Systems Modeling by Thomas J. Anastasio.

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References

Anastasio, T. J. (2009). Tutorial on neural systems modeling. Sinauer Associates, Incorporated.

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Researchers should cite this work as follows:

  • Lisa Sproat (2013), "[Illinois]: Posterior probability of a target given input for two senses (Bayes')," http://nanohub.org/resources/bisensorybayes. (DOI: 10.4231/D3DZ0320N).

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