Computer architecture -- Design, Neural networks (Computer science) -- Design and construction
The goal of this work is to explore applications of reservoir computing in biomolecular computation. Reservoir computing is a unique model for representing a mapping from one instance in time to a specific output. A neural network of randomly connected neurons is linked with a single output neuron or multiple output neurons. The output neurons are capable of mapping inputs to desired outputs using adaptable algorithms. This framework is investigated by using the Python programming language and object oriented design and programming. Neurons are created in programs by bundling information like input data and attributes of the network, which utilize methods (for instance the sum of a dot product, the hyperbolic tangent function) to operate on data (e.g. arbitrary input arrays, two variable binary inputs). This work is motivated by the idea of using adaptable algorithms instead of hardcoding information to solve classification problems in biomolecular computation, such as identifying molecular information like presence of a virus.
Fleetwood, Matthew, "Emerging Adaptive Architectures for Biomolecular Computation" (2016). Undergraduate Research & Mentoring Program. 8.