Please help us continue to improve nanoHUB operation and service by completing our survey - http://bit.ly/nH-survey14. Thank you - we appreciate your time. close

Support

Support Options

Submit a Support Ticket

 

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

By Lisa Sproat

University of Illinois at Urbana-Champaign

Trains a single sigmoidal unit using the delta rule to estimate posterior target probability given sensory input of two modalities (i.e., visual and auditory)

Launch Tool

You must login before you can run this tool.

Version 1.0a - published on 19 Aug 2013

doi:10.4231/D3959C76N cite this

This tool is closed source.

View All Supporting Documents

    bisensdelta1 bisensedelta2 bisensedelta3

Category

Tools

Published on

Abstract

This tool trains a single sigmoidal unit using the delta rule to estimate posterior target probability 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.

Sponsored by

References

Anastasio, Thomas J. Tutorial on Neural Systems Modeling. Sunderland: Sinauer Associates, 2010. Print.

Cite this work

Researchers should cite this work as follows:

  • Lisa Sproat (2013), "[Illinois]: Posterior probability of a target given input for two senses (delta)," http://nanohub.org/resources/bisensorydelta. (DOI: 10.4231/D3959C76N).

    BibTex | EndNote

Tags

nanoHUB.org, a resource for nanoscience and nanotechnology, is supported by the National Science Foundation and other funding agencies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.