Correctness, efficiency, extendability and maintainability in neural network simulation

A large number of neural network simulators are publicly available to researchers, many free of charge [11]. However, when a new paradigm is being developed, as is often the case, the advantages of using existing simulators decrease, causing most researchers to write their own software. It has been estimated that 85% of neural network researchers write their own simulators [11]. We present techniques and principles for the implementation of neural network simulators. First and foremost, we discuss methods for ensuring the correctness of results - avoiding duplication, automating common tasks, using assertions liberally, implementing reverse algorithms, employing multiple algorithms for the same task, and using extensive visualization. Secondly, we discuss efficiency concerns, including using appropriate granularity object-oriented programming, and pre-computing information whenever possible.