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首页> 美国卫生研究院文献>PLoS Computational Biology >Firing-rate based network modeling of the dLGN circuit: Effects of cortical feedback on spatiotemporal response properties of relay cells
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Firing-rate based network modeling of the dLGN circuit: Effects of cortical feedback on spatiotemporal response properties of relay cells

机译：dLGN电路的基于速率的网络建模：皮质反馈对中继电池的时空响应特性的影响

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Visually evoked signals in the retina pass through the dorsal geniculate nucleus (dLGN) on the way to the visual cortex. This is however not a simple feedforward flow of information: there is a significant feedback from cortical cells back to both relay cells and interneurons in the dLGN. Despite four decades of experimental and theoretical studies, the functional role of this feedback is still debated. Here we use a firing-rate model, the extended difference-of-Gaussians (eDOG) model, to explore cortical feedback effects on visual responses of dLGN relay cells. For this model the responses are found by direct evaluation of two- or three-dimensional integrals allowing for fast and comprehensive studies of putative effects of different candidate organizations of the cortical feedback. Our analysis identifies a special mixed configuration of excitatory and inhibitory cortical feedback which seems to best account for available experimental data. This configuration consists of (i) a slow (long-delay) and spatially widespread inhibitory feedback, combined with (ii) a fast (short-delayed) and spatially narrow excitatory feedback, where (iii) the excitatory/inhibitory ON-ON connections are accompanied respectively by inhibitory/excitatory OFF-ON connections, i.e. following a phase-reversed arrangement. The recent development of optogenetic and pharmacogenetic methods has provided new tools for more precise manipulation and investigation of the thalamocortical circuit, in particular for mice. Such data will expectedly allow the eDOG model to be better constrained by data from specific animal model systems than has been possible until now for cat. We have therefore made the Python tool pyLGN which allows for easy adaptation of the eDOG model to new situations.
机译：视网膜中的视觉诱发信号在到达视觉皮层的过程中穿过背膝状核（dLGN）。但是，这不是简单的信息前馈流：在dLGN中，皮层细胞有大量反馈返回中继细胞和中间神经元。尽管进行了四十年的实验和理论研究，但对于这种反馈的功能作用仍存在争议。在这里，我们使用发射率模型，即扩展的高斯差分（eDOG）模型，来探索皮质反馈对dLGN中继电池视觉响应的影响。对于该模型，可以通过直接评估二维或三维积分找到响应，从而可以快速，全面地研究皮质反馈的不同候选组织的推定效果。我们的分析确定了兴奋性皮层反馈和抑制性皮层反馈的特殊混合配置，这似乎可以最好地说明可用的实验数据。此配置包括（i）缓慢的（长延迟）和在空间上广泛的抑制性反馈，再加上（ii）快速的（短延迟）和在空间上狭窄的兴奋性反馈，其中（iii）兴奋性/抑制性ON-ON连接分别伴随有抑制性/激励性的OFF-ON连接，即遵循相反的布置。光遗传学和药物遗传学方法的最新发展为更精确地操纵和研究丘脑皮层回路，特别是对小鼠提供了新的工具。相比到目前为止，对于猫来说，此类数据有望使eDOG模型受到来自特定动物模型系统的数据的更好约束。因此，我们制作了Python工具 pyLGN ，该工具可以轻松地使eDOG模型适应新情况。

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