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The emergent neural modeling system
Brad Aisa1, Brian Mingus, Randy O'Reilly
1Computational Cognitive Neuroscience Lab, Department of Psychology, University of Colorado at Boulder, USA. Brad.Aisa@colorado.edu
Summary
Emergent is a powerful simulation tool for complex neural systems, balancing biological detail with computational efficiency for researchers and students. Its intuitive interface and extensible design facilitate rapid development of cognitive models.
Area of Science:
- Computational Neuroscience
- Cognitive Modeling
Background:
- Emergent builds upon decades of research in neural network algorithms and modeling principles from PDP++ and PDP.
- It was released in August 2007 as a redesigned, efficient workspace for academic research and student learning.
Purpose of the Study:
- To provide a powerful, yet accessible, tool for simulating biologically plausible, complex neural systems.
- To offer an engaging and easy-to-navigate environment for both students and advanced researchers.
- To balance detailed spiking neuron models with abstract systems for rapid cognitive model development.
Main Methods:
- Features a modern, intuitive interface with hierarchical, tree-based navigation and drag-and-drop reorganization.
- Includes high-level simulation constructs (Layers, Projections), diverse algorithms, data handling, and a virtual environment for cognitive agents.
- Offers extensibility via runtime plugins, a shell with a debugger, and a symmetric scripting language.
Main Results:
- Provides an efficient workspace for academic research and an engaging environment for students.
- Facilitates rapid development and execution of complex cognitive models.
- Maintains biological plausibility while striking a balance between detailed and abstract modeling approaches.
Conclusions:
- Emergent serves as a versatile and powerful platform for advancing research in computational neuroscience and cognitive modeling.
- Its design caters to a wide range of users, from students learning about neural systems to advanced researchers developing complex simulations.
- The software's balance of detail and abstraction enables the creation of biologically plausible cognitive models efficiently.
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