MICROCIRCUITRY OF PARALLEL CHANNELS IN VISION

人类的视觉表现很大程度上取决于电路的效率 将中心凹锥体与神经节细胞连接起来。我们的宏伟目标是发现 这是这种效率的基础。在标准视图中，所有信息 发往皮质的数据由两个数组“多路传输”：P/MIDGET(95% 中心凹神经节细胞)和M/阳伞(5%)。然而，我们目前的研究 提示非侏儒细胞比怀疑的更普遍(&gt；25%) 包括四种类型。因此，我们假设有五个神经节细胞阵列：A 光谱非选择性单元的密集阵列(侏儒)；两个更稀疏的阵列 指光谱相反的细胞(R/G&B/Y双层)；中等密度 线性、高对比度增益单元阵列(类似测试版的遮阳伞)；以及 稀疏阵列的非线性，运动敏感细胞(阿尔法式遮阳伞)。 如果得到确认，这样的阵列表明信号没有被多路传输，但 分离较早，因此允许最大限度的放大。 我们现在建议：1)通过确定以下参数来检验“五组”假设 四种类型非侏儒神经节的环路 手机。这应该表明可能与心理物理相对应。 通道，因为连接到神经节的锥体的数量和重量 小区类型，加上它的采样频率，设置的信息容量 数组。2)检验假设人类R和G侏儒神经节细胞 接收不同数量的突触。这将把我们目前的研究联系起来 关于猕猴对人类回路的研究，并测试我们对光谱的识别 对手对双层细胞的输入。3)确定无长突回路以 神经节细胞。无长突触数量众多(例如，50%的突触 到侏儒细胞)，但它们的电路可能服务于非线性 像增益控制这样的机制是完全未知的。4)确定 谷氨酸的离子型和代谢型家族中的哪些成员 受体在特定类型的双极细胞和神经节细胞上表达。一个 电路的编码容量(即信号/噪声、增益、时间带宽) 关键依赖于其突触后受体的分子特性 (例如，结合常数、通道电导和打开时间)。本地化 已识别电路中的这些已知属性将提供必要的数据 AIM 5.5)评估识别的因素(电路结构、采样 频率和突触后受体特性)影响编码效率。 将神经因素纳入“理想观察者”模型，以便与 创业因素与心理物理表现。 电路将通过电子显微镜和细胞内染料进行研究。 注射后数字光学显微镜；谷氨酸受体将 经免疫细胞化学和基因扩增鉴定 已识别的细胞；隔室模型将用于评估电路 在给定约束的情况下优化信息传输：要小(少 突触；容易饱和)，并使用嘈杂的信号机制 调职。

Human visual performance depends critically on the efficiency of circuits that connect foveal cones to ganglion cells. Our broad goal is to discover the basis for this efficiency. In the standard view all information destined for cortex is "multiplexed" by two arrays: P/midget (95% of foveal ganglion cells) and M/parasol (5%). However, our current studies suggest that non-midget cells are more prevalent than suspected (>25%) and comprise four types. Thus, we hypothesize five ganglion cell arrays: a dense array of spectrally nonselective cells (midget); two sparser arrays of spectrally opponent cells (R/G & B/Y bistratified); a moderately dense array of linear, high contrast gain cells (beta-like parasol); and a sparse array of non-linear, motion-sensitive cells (alpha-like parasol). If confirmed, such arrays suggest that signal are not multiplexed but segregated early, thus permitting maximum amplification. We now propose: 1) Test the "five array" hypothesis by determining quantitatively the circuits for all four types of non-midget ganglion cell. This should suggest possible correspondences to psychophysical channels because the number and weighting of cones connected to a ganglion cell type, plus its sampling frequency, set the information capacity of the array. 2) Test hypothesis that R and G midget ganglion cells in human receive different numbers of synapses. This will link our current studies on macaque to human circuitry and test our identification of spectrally opponent inputs to bistratified cells. 3) Determine amacrine circuits to ganglion cells. Amacrine synapses are numerous (e.g., 50% of the synapses to midget cells), but their circuits, which probably serve nonlinear mechanisms such as gain control, are completely unknown. 4) Determine which members of the ionotropic and metabotropic families of glutamate receptor are expressed on specific types of bipolar and ganglion cell. A circuit's coding capacity (i.e., signal/noise, gain, temporal bandwidth) depends critically on molecular properties of its postsynaptic receptors (e.g., binding constant, channel conductance and open time). Localizing these known properties in identified circuits will provide data essential to AIM 5. 5) Assess how identified factors (circuit structure, sampling frequency, and postsynaptic receptor properties) affect coding efficiency. Incorporate neural factors into "ideal observer" models for comparison to preneural factors and psychophysical performance. Circuits will be studied by electron microscopy and by intracellular dye injection followed by digital light microscopy; glutamate receptors will be identified by immunocytochemistry and amplification of mRNA in identified cells; compartmental models will be used to assess how circuits optimize information transfer given their constraints: to be small (few synapses; prone to saturate) and to employ noisy mechanisms for signal transfer.