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A Virtual Environment Framework for Embedding Neural Models

An important aspect of spiking neural network research is the implementation of environments and scenarios to develop and test novel theories of function and adaptation. In addition to embodying a model in the physical world, there are a number of strategies for virtually embedding neural models. Two popular approaches are using existing games or creating custom environments. Both of these however, can require significant software development in addition to the difficulty in creating compelling visual elements. The visual world can not only be the most time-consuming to develop but to render as well. Furthermore, it is often the most unnecessary aspect of the research. In this work we briefly explore using the entity system paradigm as the foundation of a generic framework for developing virtual environments independent of the visualization. This is contrasted with using an object-oriented approach for the same goal. The resulting framework not only simplifies the development of novel virtual environments but provides a higher level of code reuse. keywords: Neurorobotics, Entity System Paradigm, Virtual Environments, Computational Neuroscience.