Location

Portland State University

Start Date

2-5-2018 11:00 AM

End Date

2-5-2018 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.

Description

This material is based upon work supported by the National Science Foundation under grant no. 1518833.

Persistent Identifier

http://archives.pdx.edu/ds/psu/25101

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May 2nd, 11:00 AM May 2nd, 1:00 PM

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.