Testing efficient coding in realistic models of the retinal network

该项目旨在开发视网膜兴奋通路的详细模型，并测试其是否遵循有效的视觉信息编码策略。为此，我们将首先获取实验数据来分解视网膜处理的不同步骤。然后，我们将使用这些数据来建立视网膜兴奋通路的模型，该模型可以预测和解释复杂的输入，即自然图像，是如何在视觉前端处理的。最后，我们将使用这些模型来检验这一途径的组织与高效编码原则兼容的假设。视网膜的兴奋途径包括三个步骤：首先，光感受器将光转化为电活动，并通过特殊的谷氨酸能突触将信号传递到双极细胞(BCS)。接下来，BCS从几个光感受器中汇集，并通过另一个带状突触将它们的信号再次传递到视网膜神经节细胞(RGC)。许多研究已经描述了沿着这一途径的加工过程，然而，当呈现自然图像时，上游加工步骤如何影响RGC的反应特性还远未被理解。在这里，我们将记录RGC对自然图像的反应，并使用新的工具来研究BCS的贡献。首先，为了表征兴奋性输入冲击RGC及其产生的突触后电位，我们将分别使用基因编码的谷氨酸和电压传感器使用双光子(2P)成像记录BC输出和RGC树突电压，同时向光感受器显示自然图像。其次，为了研究BC输出如何在RGC水平上整合以生成棘波序列，我们将结合先进的2P数字全息术和光遗传学来选择性地刺激单个BC，同时使用多电极阵列(MEA)记录这种刺激对RGC棘波的影响。在这里，刺激模式将再现BCS对闪光的自然图像的反应。接下来，我们将构建一个整合这些数据的模型：BC输出、RGC树突电压和RGC尖峰对自然图像的响应，以及RGC对全息BC刺激的响应。将这些不同的数据--包括突触输出、突触后电压和尖峰，以及不同模式的时空刺激--整合到一个单一的模型中是一个新的挑战。然而，我们预计，建立和测试这样的模型将为视网膜兴奋通路如何处理自然图像提供前所未有的洞察力。最后，有了这条途径的准确模型，我们将能够定量测试其组织是否与有效的编码原则兼容。为此，我们将利用新的方法来测试复杂的非线性模型是否正在优化信息传输。

This project aims at developing a detailed model of the excitatory pathway of the retina and to test if it follows an efficient coding strategy of visual information. For this, we will first acquire experimental data to decompose the different steps of retinal processing. We will then use these data to build models of the retinal excitatory pathway that can predict and explain how complex inputs, i.e. natural images, are processed at the frontend of vision. Finally, we will use these models to test the hypothesis that the organization of this pathway is compatible with the principles of efficient coding. The retina’s excitatory pathway consists of three steps: First, photoreceptors transduce light into electrical activity and transmit the signal via specialized glutamatergic (“ribbon”) synapses to bipolar cells (BCs). Next, BCs pool from several photoreceptors and relay their signal again via another ribbon synapse to retinal ganglion cells (RGCs). Many studies have characterized the processing along this pathway, however, how upstream processing steps shape a RGC’s response properties when presenting natural images are far from understood. Here, we will record the responses of RGCs to natural images and use novel tools to investigate the contribution of BCs. First, to characterize the excitatory input impinging on RGCs and the resulting postsynaptic potentials, we will record BC output and RGC dendritic voltage using two-photon (2P) imaging with genetically-encoded glutamate and voltage sensors, respectively, while showing natural images to the photoreceptors. Second, to study how the BC output is integrated at the RGC level to generate spike trains, we will combine advanced 2P digital holography with optogenetics to selectively stimulate individual BCs while recording the impact of this stimulation on the RGC spiking using multielectrode arrays (MEAs). Here, the stimulation patterns will reproduce how BCs respond to flashed natural images. Next, we will construct a model that integrates these data: BC output, RGC dendritic voltage, and RGC spiking in response to natural images, and RGC responses to holographic BC stimulation. Integrating these heterogeneous data – consisting of synaptic output, postsynaptic voltage and spikes, as well as different modes of spatio-temporal stimulation – in a single model is a novel challenge. However, we expect that building and testing such a model will give unprecedented insight into how natural images are processed by the retinal excitatory pathway. Finally, having an accurate model of this pathway, we will be able to test quantitatively if its organization is compatible with efficient coding principles. For this, we will take advantage of novel methods to test if complex, non-linear models are optimizing information transmission.