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首页> 外文期刊>Journal of Computational Neuroscience >Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity
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Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity

机译：LGN输入到V1皮层的即开即用与泊松模型：噪声较大的输入会降低方向选择性

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摘要

One of the reasons the visual cortex has at tracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spik ing with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behav ior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical char acteristics of LGN spike trains are important for V1's function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes.
机译：视觉皮层吸引了计算神经科学兴趣的原因之一是它具有定义明确的输入。丘脑的外侧膝状核（LGN）是初级皮层（V1）的视觉信号源。大多数大型皮质网络模型将LGN神经元的尖峰序列近似为简单的Poisson点过程。但是，许多研究表明，早期视觉通路中的神经元能够以较高的时间精度突刺，并且其放电并不像泊松样。为了了解LGN中的响应变异性如何影响V1的行为，我们研究了模型V1神经元的响应特性，这些神经元从LGN细胞接收纯前馈输入，建模为嘈杂的泄漏集成并发射（NLIF）神经元或非均匀泊松过程。我们首先证明NLIF模型能够重现LGN神经元的许多实验观察到的统计特性。然后，我们表明，将V1神经元的LGN输入建模为一组NLIF神经元的V1模型产生的定向选择性要高于使用Poisson LGN输入的模型。第二个结果表明，LGN尖峰序列的统计特性对于V1的功能很重要。我们得出的结论是，V1的生理动机模型需要包括更现实的LGN峰值序列，其噪声要比非均匀泊松过程少。

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