[Illiniois]: SynchUp

By Bara Saadah1; Nahil Sobh1; AbderRahman N Sobh1

1. University of Illinois at Urbana-Champaign

This tool computes synchronous updates of autoassociative networks.

Launch Tool

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Archive Version 1.2a
Published on 19 Aug 2013 All versions

doi:10.4231/D35H7BT5T cite this

This tool is closed source.

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Tools

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Abstract

Recurrent networks can used as pattern auto-associators and can act as content addressable memories, and can serve as models for certain types of neurobiological memory. They are often used to model the hippocampus, a brain region closely linked to memory formation and recall. Recurrent neural networks are dynamic systems that process signals in time. The ones we consider in the tool consist of one layer of neural units that are all interconnected and are intended as stored memory states. This tool is then able to recall patterns given hints in the form of incomplete patterns.

Sponsored by

NanoBio Node, University of Illinois Champaign-Urbana

References

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

Cite this work

Researchers should cite this work as follows:

  • Bara Saadah, Nahil Sobh, AbderRahman N Sobh (2015), "[Illiniois]: SynchUp," https://nanohub.org/resources/synchup. (DOI: 10.4231/D35H7BT5T).

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Tags

  1. nano/bio
  2. NanoBio Node at Illinois