[Illinois]: AsynchUp

By Bara Saadah1, Nahil Sobh1, AbderRahman N Sobh1, NanoBio Node1, Jessica S Johnson1

1. University of Illinois at Urbana-Champaign

This tool computes asynchronous updates of autoassociative networks.

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Version 1.2c - published on 25 Feb 2015

doi:10.4231/D3F47GV5M cite this

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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. However, this tool implements asynchronous updates rather than synchronous. This makes it more stable.

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NanoBio Node, University of Illinois Champaign-Urbana


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; NanoBio Node; Jessica S Johnson (2015), "[Illinois]: AsynchUp," http://nanohub.org/resources/asynchup. (DOI: 10.4231/D3F47GV5M).

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