Title of Poster / Presentation
Using Reservoir Computing to Build a Robust Interface with DNA Circuits in Determining Genetic Similarities Between Pathogens
Presentation Type
Poster
Location
Portland State University
Start Date
5-2-2018 11:00 AM
End Date
5-2-2018 1:00 PM
Subjects
Computational neuroscience, Neural networks (Computer science)
Abstract
As computational power increases, the field of neural networks has advanced exponentially. In particular recurrent neural networks (RNNs) are being utilized to simulate dynamic systems and to learn to predict time series data. Reservoir computing is an architecture which has the potential to increase training speed while reducing computational costs. Reservoir computing consists of a RNN with a fixed connections “reservoir” while only the output layer is trained. The purpose of this research is to explore the effective use of reservoir computing networks with the eventual application towards use in a DNA based molecular computing reservoir for use in pathogen detection.
Rights
© Copyright the author(s)
IN COPYRIGHT:
http://rightsstatements.org/vocab/InC/1.0/
This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
DISCLAIMER:
The purpose of this statement is to help the public understand how this Item may be used. When there is a (non-standard) License or contract that governs re-use of the associated Item, this statement only summarizes the effects of some of its terms. It is not a License, and should not be used to license your Work. To license your own Work, use a License offered at https://creativecommons.org/
Persistent Identifier
http://archives.pdx.edu/ds/psu/25101
Using Reservoir Computing to Build a Robust Interface with DNA Circuits in Determining Genetic Similarities Between Pathogens
Portland State University
As computational power increases, the field of neural networks has advanced exponentially. In particular recurrent neural networks (RNNs) are being utilized to simulate dynamic systems and to learn to predict time series data. Reservoir computing is an architecture which has the potential to increase training speed while reducing computational costs. Reservoir computing consists of a RNN with a fixed connections “reservoir” while only the output layer is trained. The purpose of this research is to explore the effective use of reservoir computing networks with the eventual application towards use in a DNA based molecular computing reservoir for use in pathogen detection.
Comments/Description
This material is based upon work supported by the National Science Foundation under grant no. 1518833.