Intrinsic electrical properties shape feature selectivity in a novel retinal ganglion cell

项目摘要 神经元利用它们的固有特性对它们的突触输入进行整合和计算。什么时候 考虑到神经计算的可能机制，需要考虑的两个主要组件是网络 神经元所属的属性和神经元本身的内在属性。我提议的项目使用一个新的 在小鼠视网膜中发现了视网膜神经节细胞(RGC)类型，在实验中创建了干净的 感官计算的网络和内在方面。 我发现了一种新的视网膜神经节细胞(RGC)类型：“突发性对比抑制”(BSbC)。这个 RGC类型的主要功能分类是细胞处于ON、OFF、ON-OFF还是更罕见的状态 描述了对比度抑制(SBC)。SBC RGC将它们的放电率从基线抑制到正和 负对比度步骤。我将确定突触输入和bSbC的内在属性如何支持这一点 对比反应曲线。 我会将bSbC与称为OFFsA(OFFsA)的著名RGC类型进行比较，因为这两者 尽管细胞类型的关键功能不同，但它们具有相似的形态和相似的突触输入。我的 初步数据表明，这两种RGC类型之间的内在属性差异是原因 对于它们不同的对比度响应函数。我将比较bSbC和OFFsA的内在属性 使用电流钳和电压钳相结合的记录，单细胞RNA序列，免疫组织化学， 和药理学。 这个项目表明，在相似的突触网络中，两种不同的细胞类型可以有明显的不同 由于固有属性而产生的输出。我将能够剖析渠道类型和密度对 特定的神经元计算。这项工作将加深我们对视网膜回路的理解，更广泛地说， 它将提供一个模板，用于剖析所有神经计算中固有属性的相对贡献。

Project Summary Neurons use their intrinsic properties to integrate and perform computations on their synaptic inputs. When considering possible mechanisms of neural computations, two primary components to consider are the network that the neuron belongs to and the intrinsic properties of the neuron itself. My proposed project uses a newly discovered retinal ganglion cell (RGC) type in mouse retina to create a clean experimental separation between network and intrinsic aspects of a sensory computation. I have identified a novel retinal ganglion cell (RGC) type: the “Bursty Suppressed-by-Contrast” (bSbC). The primary functional classification for RGC types is whether the cells are ON, OFF, ON-OFF, or the more rarely described suppressed-by-contrast (SbC). SbC RGCs suppress their firing rate from baseline to both positive and negative contrast steps. I will determine how synaptic inputs and the intrinsic properties of the bSbC support this contrast response profile. I will compare the bSbC to a well-known RGC type called the OFF sustained alpha (OFFsA) because these two cell types, despite their key functional difference, have similar morphology and similar synaptic inputs. My preliminary data suggest that differences in intrinsic properties between these two RGC types are responsible for their different contrast response functions. I will compare the intrinsic properties of the bSbC and the OFFsA using a combination of current clamp and voltage clamp recordings, single cell RNAseq, immunohistochemistry, and pharmacology. This project demonstrates that two distinct cell types in similar synaptic networks can have markedly different outputs due to intrinsic properties. I will able to dissect the contribution of channel typology and density to a specific neuronal computation. This work will deepen our understanding of retinal circuitry and, more generally, it will provide a template for dissecting the relative contribution of intrinsic properties in all neural computation.