ANNarchy: a code generation approach to neural simulations on parallel hardware.
ANNarchy: a code generation approach to neural simulations on parallel hardware.
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DOI：
10.3389/fninf.2015.00019
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发表时间：
2015
影响因子：
3.5
通讯作者：
Hamker FH
中科院分区：
文献类型：
--
作者：
Vitay J;Dinkelbach HÜ;Hamker FH
许多现代神经模拟器集中于在并行硬件上模拟尖峰神经元网络。计算神经科学中的另一个重要框架，即速率编码神经网络，使用这些模拟器很难或不可能实现。我们在这里介绍ANNarchy（人工神经网络架构）神经模拟器，它允许轻松定义和模拟速率编码和尖峰网络，以及两者的组合。Python中的接口被设计为接近PyNN接口，而神经元和突触模型的定义可以使用类似于Brian神经模拟器的面向方程的数学描述来指定。此信息用于生成C++代码，这些代码将在所选并行硬件（多核系统或图形处理单元）上有效地执行模拟。有几种数值方法可用于将常微分方程转换为有效的C++代码。我们比较现有的解决方案的模拟器的并行性能。
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.