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首页> 外文会议>International Joint Conference on Neural Networks >A Reduced-Scale Cortical Network with Izhikevich's Neurons on SpiNNaker
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A Reduced-Scale Cortical Network with Izhikevich's Neurons on SpiNNaker

机译：SpiNNaker上带有Izhikevich神经元的缩小规模皮层网络

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Following the initial implementation of a full-scale spiking neural network (SNN) of the cortical microcircuit on NEST, the work was replicated to simulate on SpiNNaker, the Juelich CPU cluster, and the Sussex GPU cluster, in order to compare the performances on the different platforms. All of these researches use the Leaky Integrate and Fire (LIF) model as the basic unit of spiking neurons. In comparison, Izhikevich's spiking neuron models (IZK) can mimic a larger variety of known cortical neuronal dynamics. In spite of this versatility, the IZK neuron is easy to implement and computes fast. In this work, we implement the above-mentioned cortical microcircuit at a reduced-scale and using IZK neurons on SpiNNaker. This is aligned with our ongoing research on a reduced-scale thalamocortical circuit of vision with changing IZK neuron dynamics on SpiNNaker. We validate our SNN with the LIF-based full-scale cortical microcircuit by providing Poisson noise inputs, and measuring objectively the outputs in terms of spike rate, irregularity and synchrony. Our reduced-scale SNN shows similar dynamics to the full-scale SNN and operates within the Asynchronous Irregular regime defined by set bounds on the three quantitative attributes. Next, we test our SNN with inputs from a Dynamic Vision Sensor- (DVS-)based electronic retina (e-retina) that converted a simple periodic environmental input to spike trains. With current parameter settings, the model output identifies the low-frequency, but not the high frequency periodic inputs.
机译：在NEST上初步实现了皮质微电路的全尺度尖峰神经网络（SNN）后，在SpiNNaker、Juelich CPU集群和Sussex GPU集群上进行了模拟，以比较不同平台上的性能。所有这些研究都使用泄漏积分和火灾（LIF）模型作为尖峰神经元的基本单元。相比之下，Izhikevich的尖峰神经元模型（IZK）可以模拟更多已知的皮层神经元动力学。尽管IZK神经元具有这种多功能性，但它易于实现，计算速度也很快。在这项工作中，我们在SpiNNaker上使用IZK神经元以缩小的规模实现了上述皮质微电路。这与我们正在进行的研究一致，该研究是关于在SpiNNaker上改变IZK神经元动力学的丘脑皮层视觉回路的缩小规模。我们通过提供泊松噪声输入，并客观测量尖峰率、不规则性和同步性方面的输出，用基于LIF的全尺寸皮质微电路验证了我们的SNN。我们的缩减尺度SNN显示出与全尺度SNN类似的动态，并在三个定量属性的设定边界定义的异步不规则区域内运行。接下来，我们使用基于动态视觉传感器（DVS）的电子视网膜（e-retina）的输入测试SNN，该电子视网膜将简单的周期性环境输入转换为尖峰序列。通过当前参数设置，模型输出识别低频，但不识别高频周期性输入。

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