The role of non-canonical neural codes in behavior

项目概要/摘要 （与资助父母奖不变）信息处理取决于神经元放电率的观点 (rate编码）长期以来一直是神经生物学的中心法则。然而，其他非规范编码方案 （时间和"模拟"代码）已经被提出来携带有意义的信息， 比速率编码更强大。重要的是，该领域缺乏强大的遗传模型系统来解开非 典型的编码过程，我通过定义果蝇中的两个神经回路来解决这个问题， 用于研究时间和模拟代码。我发现时间编码是生物钟调节的基础， 睡眠在果蝇DN1p时钟神经元，而模拟和潜在的"混合"（模拟+尖峰） 编码用于实现果蝇DA-WED摄食神经元中轴突特异性饥饿处理。 作为一个模型系统，以了解尖峰时间代码如何影响分子信号和行为，我们 将专注于果蝇DN1p时钟神经元具有特定的尖峰模式，以控制睡眠质量， 突触可塑性的新形式，SPDP（Spike Pattern Dependent Plasticity）。来检查分子过程 SPDP的形成，我们将首先描述不规则尖峰内的时间结构的基本要素 DN1ps的模式，以及确定其生物物理起源。接下来，我们将研究分子机制 它在突触前尖峰模式的下游起作用，将电信号转化为生化反应。 我们还将利用果蝇遗传学的力量来描绘整个分子途径， DN1ps突触中的SPDP。 作为一个模型系统，以了解非尖峰神经元代码如何影响信号和行为，我们将 着重于果蝇DA-WED摄食神经元具有控制蛋白质饥饿行为的局部可塑性。神经 编码范例通常集中于基于"数字"全或无尖峰的模型。在哺乳动物中，纯 “模拟”编码发生在视网膜上，但最近的工作表明，模拟信号调制尖峰为基础的 信号（“混合”编码）在海马和皮质。然而，这些代码在神经网络中的功能 可塑性和行为仍不清楚。我们最近发现，"蛋白编码"轴突分支，但不是 "糖编码"轴突分支，表现出DA-WED摄食的亚阈值膜电位波动 神经元，轻度蛋白质剥夺。在严重的蛋白质剥夺后，这种类似信号相互作用 通过尖峰事件产生"混合"处理，以实现更强和更持久的蛋白质喂养， 行为因此，我们将研究介导“模拟”和“杂交”信号的分子过程，以及如何 “混合”代码可能是局部分支特异性可塑性的基础。总之，这些使用果蝇的研究 DN1p时钟神经元和DA-WED喂养神经元应该阐明非规范的基本原则， 神经代码，确定这些神经代码在持久行为和可塑性中的作用，并确定其 潜在的分子机制。

Project Summary/Abstract (Unchanged from the funded parent award) The idea that information processing depends on neuronal firing rate (rate coding) has long been a central dogma in neurobiology. However, other non-canonical coding schemes (temporal and “analog” codes) have been proposed to carry meaningful information and be more computationally powerful than rate coding. Importantly, the field has lacked powerful genetic model systems to disentangle non- canonical coding processes, and I addressed this gap by defining two neural circuits in Drosophila that can be used to study temporal and analog codes. I found that temporal coding underlies the circadian regulation of sleep in the Drosophila DN1p clock neurons, whereas analog and potentially “hybrid” (analog + spiking) codes are used to achieve axon-specific hunger processing in Drosophila DA-WED feeding neurons. As a model system to understand how spiking temporal codes impact molecular signaling and behavior, we will focus on Drosophila DN1p clock neurons having specific spiking patterns to control sleep quality through a novel form of synaptic plasticity, SPDP (Spike Pattern Dependent Plasticity). To examine the molecular process of SPDP formation, we will first characterize essential elements of the temporal structures within irregular spiking patterns in DN1ps, as well as identify their biophysical origins. Next, we will investigate molecular mechanisms that act downstream of presynaptic spiking patterns to transform electrical signals into biochemical responses. We will also leverage the power of Drosophila genetics to delineate the entire molecular pathway required for SPDP in DN1ps synapses. As a model system to understand how nonspiking neuronal codes impact signaling and behavior, we will focus on Drosophila DA-WED feeding neurons having local plasticity to control protein hunger behavior. Neural coding paradigms have generally focused on “digital” all-or-none spike-based models. In mammals, pure “analog” coding occurs in the retina, but recent work has shown that analog signaling modulates spike-based signaling (“hybrid” coding) in the hippocampus and cortex. However, the function of these codes in neural plasticity and behavior remains unclear. We recently discovered that the “protein coding” axonal branch, but not the “sugar coding” axonal branch, exhibits sub-threshold membrane potential fluctuations of DA-WED feeding neurons, following mild protein deprivation. Following severe protein deprivation, such analog signaling interacts with spiking events to generate “hybrid” processing to achieve stronger and longer-lasting protein feeding behavior. Thus, we will study the molecular processes mediating “analog” and “hybrid” signaling and how “hybrid” codes may underlie localized branch-specific plasticity. In conclusion, these studies using Drosophila DN1p clock neurons and DA-WED feeding neurons should elucidate fundamental principles for non-canonical neural codes, determine the role of these neural codes in long-lasting behaviors and plasticity, and identify their underlying molecular mechanisms.