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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
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ANNarchy: a code generation approach to neural simulations on parallel hardware.
Julien Vitay1, Helge Ü Dinkelbach1, Fred H Hamker2
1Department of Computer Science, Chemnitz University of Technology Chemnitz, Germany.
Frontiers in Neuroinformatics
|August 19, 2015
Summary
ANNarchy is a new neural simulator for computational neuroscience. It enables easy simulation of rate-coded, spiking, and hybrid neural networks on parallel hardware, offering a flexible alternative to existing simulators.
Area of Science:
- Computational Neuroscience
- Artificial Intelligence
- Neuroscience
Background:
- Modern neural simulators primarily focus on spiking neuron networks.
- Rate-coded neural networks are difficult to simulate with existing tools.
- A need exists for a simulator supporting diverse neural network models.
Purpose of the Study:
- Introduce ANNarchy, a novel neural simulator.
- Enable simulation of rate-coded, spiking, and hybrid neural networks.
- Provide a flexible and efficient simulation platform.
Main Methods:
- ANNarchy utilizes an equation-oriented mathematical description for neuron and synapse models.
- It generates efficient C++ code for parallel hardware (multi-core CPUs and GPUs).
- The simulator offers multiple numerical methods for differential equation transformation.
Main Results:
- ANNarchy allows flexible definition and simulation of combined rate-coded and spiking networks.
- The Python interface is designed to be similar to PyNN.
- Performance is compared against existing parallel simulation solutions.
Conclusions:
- ANNarchy provides a unified framework for simulating diverse neural network types.
- The simulator offers efficient performance on parallel hardware.
- It represents a significant advancement for computational neuroscience research.
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