Testing efficient coding in realistic models of the retinal network

在视网膜网络的真实模型中测试有效编码

• 批准号: 505379160
• 负责人: Professor Dr. Philipp Berens
• 金额: --
• 依托单位: Werner Reichardt-Centrum für integrative Neurowissenschaften (CIN)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/505379160?language=en
• 关键词: Testing; efficient; coding; realistic; models

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.

本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构建视网膜兴奋性通路的模型，这些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测试该途径的组织与高效编码原则兼容的假设。视网膜的兴奋性通路由三个步骤组成：首先，光感受器将光转换成电活动，并通过专门的神经元能（“带状”）突触将信号传输到双极细胞（BC）。接下来，BC从几个光感受器汇集，并通过另一个带状突触将其信号再次传递到视网膜神经节细胞（RGC）。许多研究的特点是处理沿着这条途径，然而，如何上游处理步骤形状的RGC的响应特性时，呈现自然的图像还远未了解。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，为了表征撞击RGC的兴奋性输入和由此产生的突触后电位，我们将分别使用具有遗传编码的谷氨酸和电压传感器的双光子（2 P）成像来记录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水平上整合以产生尖峰序列，我们将联合收割机与光遗传学相结合，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尖峰的影响。在这里，刺激模式将再现BC如何响应闪现的自然图像。接下来，我们将构建一个模型，整合这些数据：BC输出，RGC树突电压，RGC尖峰响应自然图像，以及RGC响应全息BC刺激。将这些异构数据-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-整合到一个模型中是一个新的挑战。然而，我们希望建立和测试这样一个模型将提供前所未有的洞察自然图像是如何处理视网膜兴奋性通路。最后，有了这个通路的精确模型，我们将能够定量地测试它的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优化了信息传输。