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* WikiCite / Zotero Entry
  • Title: The emergent neural modeling system.
  • Author(s): Aisa, Brad and Mingus, Brian and O'Reilly, Randy
  • Journal: Neural Networks
  • Date: October 2008
  • Volume: 21
  • Issue: 8
  • Pages: 1146–1152
  • URL: [1]

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AisaMingusOReilly08 Aisa, B., Mingus, B. & O'Reilly, R. (2008). The emergent neural modeling system. Neural networks, 21, 1146--1152. pdf icon.png AisaMingusOReilly08.pdf (Web)


Emergent ( is a powerful tool for the simulation of biologically plausible, complex neural systems that was released in August 2007. Inheriting decades of research and experience in network algorithms and modeling principles from its predecessors, PDP++ and PDP, Emergent has been redesigned as an efficient workspace for academic research and an engaging, easy-to-navigate environment for students. The system provides a modern and intuitive interface for programming and visualization centered around hierarchical, tree-based navigation and drag-and-drop reorganization. Emergent contains familiar, high-level simulation constructs such as Layers and Projections, a wide variety of algorithms, general-purpose data handling and analysis facilities and an integrated virtual environment for developing closed-loop cognitive agents. For students, the traditional role of a textbook has been enhanced by wikis embedded in every project that serve to explain, document, and help newcomers engage the interface and step through models using familiar hyperlinks. For advanced users, the software is easily extensible in all respects via runtime plugins, has a powerful shell with an integrated debugger, and a scripting language that is fully symmetric with the interface. Emergent strikes a balance between detailed, computationally expensive spiking neuron models and abstract, Bayesian or symbolic systems. This middle level of detail allows for the rapid development and successful execution of complex cognitive models while maintaining biological plausibility.