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首页> 美国卫生研究院文献>The Journal of Neuroscience >Dynamics of Local Input Normalization Result from Balanced Short- and Long-Range Intracortical Interactions in Area V1
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Dynamics of Local Input Normalization Result from Balanced Short- and Long-Range Intracortical Interactions in Area V1

机译：V1区域中短程和长程皮层内相互作用的平衡导致局部输入归一化的动力学

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To efficiently drive many behaviors, sensory systems have to integrate the activity of large neuronal populations within a limited time window. These populations need to rapidly achieve a robust representation of the input image, probably through canonical computations such as divisive normalization. However, little is known about the dynamics of the corticocortical interactions implementing these rapid and robust computations. Here, we measured the real-time activity of a large neuronal population in V1 using voltage-sensitive dye imaging in behaving monkeys. We found that contrast gain of the population increases over time with a time constant of ∼30 ms and propagates laterally over the cortical surface. This dynamic is well accounted for by a divisive normalization achieved through a recurrent network that transiently increases in size after response onset with a slow swelling speed of 0.007–0.014 m/s, suggesting a polysynaptic intracortical origin. In the presence of a surround, this normalization pool is gradually balanced by lateral inputs propagating from distant cortical locations. This results in a centripetal propagation of surround suppression at a speed of 0.1–0.3 m/s, congruent with horizontal intracortical axons speed. We propose that a simple generalized normalization scheme can account for both the dynamical contrast response function through recurrent polysynaptic intracortical loops and for the surround suppression through long-range monosynaptic horizontal spread. Our results demonstrate that V1 achieves a rapid and robust context-dependent input normalization through a timely push–pull between local and lateral networks. We suggest that divisive normalization, a fundamental canonical computation, should be considered as a dynamic process.
机译：为了有效地驱动许多行为，感觉系统必须在有限的时间范围内整合大型神经元群体的活动。这些总体可能需要通过规范计算（例如除法归一化）来快速实现输入图像的鲁棒表示。但是，对于实现这些快速而可靠的计算的皮层相互作用的动力学知之甚少。在这里，我们使用行为灵敏的猴子中的电压敏感染料成像技术测量了V1中大量神经元种群的实时活动。我们发现，总体的对比度增益随时间的变化而增加，时间常数约为30 ms，并在皮质表面横向传播。这种动态很好地归因于通过递归网络实现的分裂归一化，该递归网络在响应发作后大小突然增加，0.007–0.014 m / s的缓慢溶胀速度提示多突触皮层内起源。在存在环绕声的情况下，此标准化池由从远处皮质位置传播的横向输入逐渐平衡。这导致围绕抑制的向心传播，速度为0.1-0.3 m / s，与水平皮层内轴突速度一致。我们建议一个简单的通用归一化方案可以解决通过反复多突触皮层内循环的动态对比响应函数和通过远程单突触水平扩展的周围抑制。我们的结果表明，V1通过在本地网络和横向网络之间进行及时的推挽操作，实现了快速，强大的上下文相关输入标准化。我们建议，除法归一化（一个基本的规范计算）应被视为动态过程。

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