First Advisor

Christof Teuscher

Date of Publication

Winter 2-23-2018

Document Type


Degree Name

Master of Science (M.S.) in Electrical and Computer Engineering


Electrical and Computer Engineering




Neural networks (Computer science) -- Design and construction, Machine learning



Physical Description

1 online resource (xv, 100 pages)


Reservoir Computing (RC) is an emerging Machine Learning (ML) paradigm. RC systems contain randomly assembled computing devices and can be trained to solve complex temporal tasks. These systems are computationally cheaper to train than other ML paradigms such as recurrent neural networks, and they can also be trained to solve multiple tasks simultaneously. Further, hierarchical RC systems with fixed topologies, were shown to outperform monolithic RC systems by up to 40% when solving temporal tasks. Although the performance of monolithic RC networks was shown to improve with increasing network size, building large monolithic networks may be challenging, for example because of signal attenuation.

In this research, larger hierarchical RC systems were built using a network generation algorithm. The benefits of these systems are presented by evaluating their accuracy in solving three temporal problems: pattern detection, food foraging, and memory recall. This work also demonstrates the functionality of random Boolean networks being used as reservoirs. Networks with up to 5,000 neurons were used to 200 sequences from memory and to identify X or O patterns temporally. Also, a Genetic Algorithm (GA) was used to train different types of hierarchical RC networks, to find optimal solutions for food-foraging tasks.

This research shows that about 80% of the possible different hierarchical configurations of RC systems can outperform monolithic RC systems by up to 60% while solving complex temporal tasks. These results suggest that hierarchical random Boolean network RC systems can be used to solve temporal tasks, instead of building large monolithic RC systems.


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