Introduction

Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions.

Publications

  1. ANNarchy: a code generation approach to neural simulations on parallel hardware.
    Cite this
    Vitay J, Dinkelbach HÜ, Hamker FH, 2015-01-01 - Frontiers in neuroinformatics

Credits

  1. Julien Vitay
    Developer

    Department of Computer Science, Chemnitz University of Technology Chemnitz, Germany

  2. Helge Ü Dinkelbach
    Developer

    Department of Computer Science, Chemnitz University of Technology Chemnitz, Germany

  3. Fred H Hamker
    Investigator

    Department of Computer Science, Chemnitz University of Technology Chemnitz, Germany

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Summary
AccessionBT005695
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesC++
User InterfaceTerminal Command Line
Download Count0
Country/RegionGermany
Submitted ByFred H Hamker