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The state of MIIND
Marc de Kamps1, Volker Baier, Johannes Drever
1Biosystems Group, School of Computing, University of Leeds, LS2 9JT Leeds, United Kingdom. dekamps@comp.leeds.ac.uk
Summary
MIIND is a C++ framework that accelerates cognitive neuroscience model development. It provides reusable modules for sparse networks and dynamic simulations, supporting detailed and extensible neural models.
Area of Science:
- Cognitive Neuroscience
- Computational Neuroscience
- Computational Biology
Background:
- Developing computational models in cognitive neuroscience is time-consuming.
- Existing frameworks may impose specific modeling philosophies.
- Need for flexible and reusable tools to accelerate research.
Purpose of the Study:
- Introduce MIIND (Multiple Interacting Instantiations of Neural Dynamics), a modular C++ framework.
- Reduce development time for cognitive neuroscience models.
- Support for various modeling approaches and biological detail.
Main Methods:
- MIIND offers reusable code modules (libraries) for common modeling tasks.
- Includes libraries for sparse network implementation (SparseImplementationLib, LayerMappingLib).
- Provides a generic simulation framework (DynamicLib) with specific implementations (e.g., Wilson-Cowan, Ornstein-Uhlenbeck, leaky-integrate-and-fire).
Main Results:
- MIIND facilitates the implementation of sparse networks and dynamic neural processes.
- Supports model detailing (adding biological complexity) and extensibility (model reuse).
- Successfully applied to instantiate neural models of visual attention and artificial vision (e.g., HMAX).
Conclusions:
- MIIND is a versatile framework for accelerating cognitive neuroscience research.
- Its modular design and focus on extensibility promote efficient model development.
- Future applications include integrating attention mechanisms into artificial vision models.
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