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A simple model of retina-LGN transmission

机译：视网膜LGN传输的简单模型

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To gain a deeper understanding of the transmission of visual signals from retina through the lateral geniculate nucleus (LGN), we have used a simple leaky integrate and-fire model to simulate a relay cell in the LGN. The simplicity of the model was motivated by two questions: (1) Can an LGN model that is driven by a retinal spike train recorded as synaptic ('S') potentials, but does not include a diverse array of ion channels, nor feedback inputs from the cortex, brain-stem, and thalamic reticular nucleus, accurately simulate the LGN discharge on a spike-for-spike basis? (2) Are any special synaptic mechanisms, beyond simple summation of currents, necessary to model experimental recordings? We recorded cat relay cell responses to spatially homogeneous small or large spots, with luminance that was rapidly modulated in a pseudo-random fashion. Model parameters for each cell were optimized with a Simplex algorithm using a short segment of the recording. The model was then tested on a much longer, distinct data set consisting of responses to numerous repetitions of the noisy stimulus. For LGN cells that spiked in response to a sufficiently large fraction of retinal inputs, we found that this simplified model accurately predicted the firing times of LGN discharges. This suggests that modulations of the efficacy of the retino-geniculate synapse by pre-synaptic facilitation or depression are not necessary in order to account for the LGN responses generated by our stimuli, and that post-synaptic summation is sufficient.
机译：为了更深入地了解视觉信号从视网膜通过外侧膝状核（LGN）的传输，我们使用了一个简单的泄漏积分和发射模型来模拟LGN中的中继单元。该模型的简单性受到两个问题的推动：（1）由视网膜尖峰序列驱动的LGN模型能否记录为突触（'S'）电位，但不包括各种离子通道，也不包括反馈输入从皮层，脑干和丘脑网状核中准确地模拟出LGN放电？ （2）除了简单地对电流进行简单的求和外，是否还需要对实验记录进行建模的特殊突触机制？我们记录了猫中继细胞对空间均匀的小点或大点的响应，并以伪随机方式快速调节了亮度。使用录音的一小段，使用Simplex算法优化每个单元的模型参数。然后在更长，独特的数据集上对模型进行测试，该数据集包括对无数刺激重复的响应。对于响应于足够大比例的视网膜输入而突增的LGN细胞，我们发现该简化模型可以准确预测LGN放电的放电时间。这表明为了解释由我们的刺激产生的LGN应答，并不需要通过突触前促进或抑制来调节视网膜-膝状突触的功效，并且突触后的总和就足够了。

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