Team InVibe, Institut de Neurosciences de la Timone, UMR 7289, CNRS and Aix-Marseille Université, 13385 Marseille Cedex 05, France.
J Neurosci. 2012 Sep 5;32(36):12558-69. doi: 10.1523/JNEUROSCI.1618-12.2012.
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.
为了有效地驱动多种行为，感觉系统必须在有限的时间窗口内整合大量神经元群体的活动。这些群体需要通过例如分档归一化等典型计算快速、稳健地对输入图像进行表示。然而，对于实现这些快速、稳健计算的皮质间相互作用的动力学过程，我们知之甚少。在这里，我们使用行为猕猴的电压敏感染料成像，测量了 V1 中一大群神经元的实时活动。我们发现，群体的对比度增益随着时间的推移而增加，时间常数约为 30 毫秒，并在皮质表面上横向传播。这种动态很好地解释了通过一个递归网络实现的分档归一化，该网络在响应开始后暂时增加大小，肿胀速度较慢，为 0.007-0.014 m/s，这表明其具有多突触皮质内起源。在存在环绕的情况下，这种归一化池会被从遥远的皮质位置横向传播的输入逐渐平衡。这导致了环绕抑制的向心性传播，速度为 0.1-0.3 m/s，与水平皮质内轴突速度一致。我们提出，一个简单的广义归一化方案可以通过局部和横向网络之间的及时推拉来解释动态对比度响应函数，以及通过长程单突触水平扩展来解释环绕抑制。我们的结果表明，V1 通过局部和横向网络之间的及时推拉来实现快速、稳健的上下文相关输入归一化。我们建议，作为一种基本的规范计算，分档归一化应该被视为一个动态过程。
