Correctness, efficiency, extendability and maintainability in neural network simulation

Steve Lawrence, Ah Chung Tsoi, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE
Pages474-479
Number of pages6
Volume1
StatePublished - 1996
EventProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA
Duration: Jun 3 1996Jun 6 1996

Other

OtherProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
CityWashington, DC, USA
Period6/3/966/6/96

All Science Journal Classification (ASJC) codes

  • Software

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    Lawrence, S., Tsoi, A. C., & Lee Giles, C. (1996). Correctness, efficiency, extendability and maintainability in neural network simulation. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 1, pp. 474-479). IEEE.