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This thesis introduces fnnlib, a C++ library for recurrent neural network simulations that I developed between October 2009 and March 2010 at Osaka University's Graduate School of Engineering. After covering the theory behind recurrent neural networks, backpropagation through time, recurrent neural networks with parametric bias, continuous-time recurrent neural networks, and echo state networks, the design of the library is explained. All of the classes as well as their interrelationships are presented along with reasons as to why certain design decisions were made. Towards the end of the thesis, a small practical example is shown. Also, fnnlib is compared to other neural network libraries.
This paper documents the development of an abstract physics layer (APL) for Simspark. After short introductions to physics engines and Simspark, reasons why an APL was developed are explained. The biggest part of this paper describes the new design and why certain design choices were made based on requirements that arose during developement. It concludes by explaining how the new design was eventually implemented and what future possibilities the new design holds.