MICROCIRCUITRY OF PARALLEL CHANNELS IN VISION

人类的视觉表现主要取决于电路的效率 连接中央凹视锥和神经节细胞我们的主要目标是发现 这是效率的基础。在标准视图中， 预定用于皮质的是由两个阵列"复用"的：P/侏儒（95%的 中央凹神经节细胞）和M/parasol（5%）。然而，我们目前的研究 这表明非侏儒细胞比怀疑的更普遍（> 25%）， 包括四种类型。因此，我们假设五个神经节细胞阵列： 光谱非选择性细胞的密集阵列（侏儒）;两个稀疏阵列 光谱对立细胞（R/G和B/Y双分层）;中等密度 线性高对比度增益单元阵列（β样遮阳伞）;以及 稀疏排列的非线性运动敏感细胞（α状阳伞）。 如果得到证实，这种阵列表明信号不是多重的， 早期分离，从而允许最大的扩增。 我们现在提出：1）通过确定以下参数来检验"五阵列"假设： 定量分析所有四种类型的非侏儒神经节的回路 cell.这应该表明可能的对应心理物理 因为连接到神经节的视锥细胞的数量和重量 细胞类型，加上其采样频率，设置的信息容量 阵2)检验假设R和G缩小人类神经节细胞 接受不同数量的突触。这将把我们目前的研究 猕猴到人类的电路，并测试我们的识别光谱 对立方输入到双分层细胞。3)确定无长突回路， 神经节细胞无长突突触数量众多（例如，50%的突触 到侏儒细胞），但它们的电路，可能是非线性的， 诸如增益控制的机制是完全未知的。4)确定 谷氨酸离子型和代谢型家族的成员 受体在特定类型的双极细胞和神经节细胞上表达。一 电路的编码容量（即，信号/噪声、增益、时间带宽） 关键取决于突触后受体的分子特性 (e.g.,结合常数、通道电导和开放时间）。定位 在识别的电路中的这些已知特性将提供数据基本 目标5 5)评估已识别的因素（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， 前神经因素和心理物理表现。 电路将通过电子显微镜和细胞内染料进行研究 注射，然后数字光学显微镜;谷氨酸受体将 通过免疫细胞化学和mRNA扩增鉴定， 识别细胞;房室模型将用于评估电路如何 优化信息传递，考虑到它们的限制：要小（少 突触;易于饱和），并采用噪声机制来进行信号传输 转移

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.