Title of Poster / Presentation
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.
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.
Description
This material is based upon work supported by the National Science Foundation under grant no. 1518833.