Unraveling the mechanisms of surround suppression in early visual processing.
Unraveling the mechanisms of surround suppression in early visual processing.
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DOI：
10.1371/journal.pcbi.1008916
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发表时间：
2021-04
影响因子：
4.3
通讯作者：
Young LS
中科院分区：
文献类型：
--
作者：
Li Y;Young LS
本文利用数学模型研究了灵长类动物视觉皮层包围抑制的机制。我们提出了一个由三个相互连接的组件组成的大规模神经回路模型：LGN和初级视觉皮层V1的两个输入层（第4Ca层和第6层），涵盖数百个超柱。解剖结构被纳入和生理参数从现实的建模工作使用。选择剩余的参数来产生模拟实验观察到的尺寸调整曲线的模型输出。我们的两个主要结果是：(i)我们发现了第6层中负责输入层环绕效果的远程连接的特征；（ii）我们发现，从第6层到第4层，净抑制反馈，即对i细胞的刺激大于对e细胞的刺激，有利于产生与实验数据一致的环绕特性。这些结果是通过参数选择和模型分析得出的。本文还讨论了非线性循环激励和抑制的影响。将我们的模型与之前的环绕抑制建模工作区分开来的一个特征是，我们试图重现现实的长度尺度，这对于与数据进行定量比较至关重要。由于其规模和大量的未知参数，该模型在计算上具有挑战性。我们演示了一种策略，首先使用线性模型定位相关参数的基线值，然后在需要的地方引入非线性。我们发现这种方法是有效的，并提出它作为一种可能性在复杂的生物系统的建模。视觉皮层是大脑皮层中处理视觉信号的部分。来自视网膜的视觉信号通过外侧膝状核（LGN）传递，并直接进入初级视觉皮层。初级视觉皮层由许多层组成。在灵长类动物初级视觉皮层的所有层中都可以观察到一种被称为“周围抑制”（surround suppression）的现象，这意味着神经元活动在对尺寸增加的刺激做出反应时减少。本文建立了由LGN输入和初级视觉皮层第4层和第6层数万个神经元群组成的大规模数学模型，研究环绕抑制的机制。该模型受到解剖结构和现实生理参数的约束。剩余的未知参数从实验数据逆向工程。我们展示了环绕抑制的特性如何取决于反馈层中的远程连接，以及从第6层到第4层的反馈电流。
This paper uses mathematical modeling to study the mechanisms of surround suppression in the primate visual cortex. We present a large-scale neural circuit model consisting of three interconnected components: LGN and two input layers (Layer 4Ca and Layer 6) of the primary visual cortex V1, covering several hundred hypercolumns. Anatomical structures are incorporated and physiological parameters from realistic modeling work are used. The remaining parameters are chosen to produce model outputs that emulate experimentally observed size-tuning curves. Our two main results are: (i) we discovered the character of the long-range connections in Layer 6 responsible for surround effects in the input layers; and (ii) we showed that a net-inhibitory feedback, i.e., feedback that excites I-cells more than E-cells, from Layer 6 to Layer 4 is conducive to producing surround properties consistent with experimental data. These results are obtained through parameter selection and model analysis. The effects of nonlinear recurrent excitation and inhibition are also discussed. A feature that distinguishes our model from previous modeling work on surround suppression is that we have tried to reproduce realistic lengthscales that are crucial for quantitative comparison with data. Due to its size and the large number of unknown parameters, the model is computationally challenging. We demonstrate a strategy that involves first locating baseline values for relevant parameters using a linear model, followed by the introduction of nonlinearities where needed. We find such a methodology effective, and propose it as a possibility in the modeling of complex biological systems. The visual cortex is a part of the cortex that processes visual signal. The visual signal from the retina is relayed through the lateral geniculate nucleus (LGN), and enters the primary visual cortex directly. The primary visual cortex consists of many layers. A phenomenon called surround suppression, which means a reduction of neuronal activities in response to stimuli of increasing size, is well-observed in all layers of the primate primary visual cortex. In this paper, we built a large scale mathematical model consisting of the LGN input and tens of thousands of neuron groups in Layer 4 and Layer 6 of the primary visual cortex to study the mechanism of surround suppression. This model is constrained by both anatomical structures and realistic physiological parameters. Remaining unknown parameters are reverse-engineered from experimental data. We showed how the character of the surround suppression depends on the long-range connections in the feedback layer, as well as the feedback current from Layer 6 to Layer 4.
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影响因子：
16.2
作者：
Ozeki, Hirofumi;Finn, Ian M.;Schaffer, Evan S.;Miller, Kenneth D.;Ferster, David
通讯作者：
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影响因子：
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通讯作者：
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影响因子：
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