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Event-related simulation of neural processing in complex visual scenes

机译:复杂视觉场景中神经处理的事件相关模拟

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Understanding the neural processing of complex visual scenes is facilitated by tools that allow efficient simulation of large neural systems. We here present an environment for the implementation of large networks of generalized integrate-and-fire neurons which uses an asynchronous event-based algorithm. It allows accurate (up to machine precision) calculation of neuronal spike times. Its efficiency arises from its ability to solve the subthreshold dynamics of the model analytically. The simulator is implemented in JAVA and it can run in parallel on a large number of computers with different architectures without the need for compilation. The neuronal network to be simulated and all parameters are defined in extendible markup language. A model of the primate early visual system is implemented. The use of the tool is illustrated by simulating the processing of both simple and complex visual scenes through retina, thalamus and primary visual cortex.
机译:允许对大型神经系统进行有效仿真的工具有助于理解复杂视觉场景的神经处理。在这里,我们介绍了一个使用通用异步事件算法的大型集成化解雇神经元网络的实现环境。它允许对神经元尖峰时间进行准确的计算(达到机器精度)。它的效率源于其通过解析解决模型的亚阈值动力学的能力。该模拟器是在JAVA中实现的,可以在具有不同体系结构的大量计算机上并行运行,而无需进行编译。用可扩展标记语言定义要模拟的神经元网络和所有参数。灵长类动物早期视觉系统的模型得以实现。通过模拟通过视网膜,丘脑和初级视觉皮层对简单和复杂视觉场景的处理,说明了该工具的使用。

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