Synaptic and Circuit Mechanisms of Olfactory Processing

这个项目旨在填补我们在算法层面上对神经计算的理解与我们的 在细胞机制的水平上理解相同的计算--即突触、通道和模式 单个神经元之间的连通性。对简单网络的研究在填补这一空白方面尤其有力。在这 建议，我们将集中在两个相对简单的网络，果蝇的触角叶和侧角。天线 脑叶是嗅觉系统的第一个大脑区域，侧角是接收大部分 触角叶轴突投射。触角叶是研究两种生物细胞机制的有用模型。 基础神经计算、增益控制和时间滤波。侧角是一种有用的模型，可用于研究 模式识别的细胞机制--在这种情况下，是通过触角叶小球的活动模式。我们会 研究三个主要问题，每个问题都与这些网络中的元胞元素和计算之间的关系有关。首先，为什么触角叶中的抑制性中间神经元如此多样化？这些中间神经元对气味浓度的增加或减少(打开或关闭细胞)或特定的气味脉冲重复频率(快或慢的细胞)做出选择性反应，它们也对不同范围的气味浓度敏感。为了验证不同中间神经元具有不同计算功能的假设，我们将使用大规模连续切片EM，并结合体内电生理学和光遗传学。第二，侧角神经元是如何采样嗅球间隙的？每个侧角神经元平均从4个肾小球接受前馈兴奋(总共50个肾小球)，但突触后侧角神经元的总数远远少于可能的肾小球组合的数量，这提出了一个问题，即每个侧角实际上以固定的方式连接在一起的肾小球组合可能有什么特殊之处。为了验证肾小球连接在一起有很强的统计规律的假设，我们将使用2P介导的对已识别的肾小球的光遗传刺激，结合来自突触后侧角神经元的全细胞记录。第三，侧角神经元如何整合它们的突触输入？如果涉及多个非线性步骤，模式识别可能会更强大，但我们不知道这种非线性实际上会在细胞机制水平上实现。为了验证侧角突触整合涉及多个非线性因素的假设，我们将使用体内电压成像和体内全细胞记录，并结合突触抑制的定向扰动。总体而言，这些研究应该会极大地促进我们对基础神经计算基础细胞和突触构件的理解。我们的长期目标是在多个粒度级别上对这些简单的网络发展一个相当完整的理解。我们预计我们的发现将产生可测试的假设和概念性方法，这将加快理解更复杂网络的进展。

This project aims to fill the gap between our understanding of neural computations at the algorithmic level and our understanding of the same computations at the level of cellular mechanisms – i.e., synapses, channels, and patterns of connectivity between individual neurons. Studies of simple networks are especially powerful in filling this gap. In this proposal, we will focus on two relatively simple networks, the Drosophila antennal lobe and lateral horn. The antennal lobe is the first brain region of the olfactory system, and the lateral horn is the brain region that receives the majority of antennal lobe axonal projections. The antennal lobe is a useful model for studying the cellular mechanisms of two fundamental neural computations, gain control and temporal filtering. The lateral horn is a useful model for studying the cellular mechanisms of pattern recognition – in this case, patterns of activity across antennal lobe glomeruli. We will investigate three main questions, each relating to the relationship between cellular elements and computations within these networks. First, why are inhibitory interneurons in the antennal lobe so diverse? These interneurons respond selectively to odor concentration increases or decreases (ON or OFF cells), or particular odor pulse repetition rates (fast or slow cells), and they are also sensitive to odor concentration over different ranges. To test the hypothesis that different interneurons have distinct computational functions, we will use large-scale serial section EM in combination with in vivo electrophysiology and optogenetics. Second, how do lateral horn neurons sample the space of olfactory glomeruli? Each lateral horn neuron receives feedforward excitation from ~4 glomeruli on average (out of 50 glomeruli in total), but the total number of postsynaptic lateral horn neurons is much smaller than the number of possible glomerular combinations, raising the question of what might be special about the glomerular combinations that actually wire together in a stereotyped fashion in every lateral horn. To test the hypothesis that there are strong statistical regularities governing which glomeruli wire together, we will use 2P-mediated optogenetic stimulation of identified glomeruli, in combination with whole cell recordings from postsynaptic lateral horn neurons. Third, how do lateral horn neurons integrate their synaptic inputs? Pattern recognition can be more powerful if it involves multiple nonlinear steps, but we do not know such nonlinearities would actually be implemented at the level of cellular mechanism. To test the hypothesis that lateral horn synaptic integration involves multiple nonlinear elements, we will use both in vivo voltage imaging and in vivo whole cell recordings, together with targeted perturbations of synaptic inhibition. As a whole, these studies should substantially advance our understanding of the cellular and synaptic building-blocks underlying fundamental neural computations. Our long-term goal is to develop a fairly complete understanding of these simple networks on multiple levels of granularity. We expect our discoveries to yield testable hypotheses and conceptual approaches which will accelerate progress in understanding more complex networks.