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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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Showing 1 to 2 of 2 (0.034 seconds)

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1

ANNarchy: a code generation approach to neural simulations on parallel hardware

Vitay, Julien, Dinkelbach, Helge Ülo, Hamker, Fred Henrik 07 October 2015 (has links) (PDF)
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.
Python
Netzwerk
Code
Parallelrechner
Technische Universität Chemnitz
Publikationsfonds
neural simulator
Python
rate-coded networks
spiking networks
parallel computing
code generation
Technische Universität Chemnitz
Publication funds
ddc:004
C++
Python
Netzwerk
Code
Parallelrechner
2

ANNarchy: a code generation approach to neural simulations on parallel hardware

Vitay, Julien, Dinkelbach, Helge Ülo, Hamker, Fred Henrik 07 October 2015 (has links)
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.
info:eu-repo/classification/ddc/004
ddc:004

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