“CRCNS: Computational principles of memory based decision making in Drosophila”

决策在很大程度上取决于存储在 大脑就像记忆一样。二选一决策过程的最好理解的模型系统之一是 果蝇的联合嗅觉记忆。在果蝇中，学习发生在凯尼恩细胞之间的突触 (KCS)和蘑菇体输出神经元(MBON)。通过将嗅觉刺激与奖励或 惩罚苍蝇可以可靠地学习区分两种最初中性的气味。与哺乳动物一样，多巴胺能 神经元(DAN)代表这一背景价位，并调节KC&GT；MBON的强度或连接性 在MB的不同隔间内突触，从而改变行为。为了理解这是如何 决策是在内存中编码的，因此我们必须首先理解在内存中执行的计算 单独的DAN-KC-MBON模块。在前期工作中，我们建立了一个现实的计算模型 包括精确突触连接的MBON-3神经元所有948个支配KCs的完整 MB的突触连接体。我们的模型结合了MBON-3的精确膜特性 我们通过在体内进行膜片钳记录获得了神经元。我们证明了MBON-3是 电调紧凑，并显示出反映生理的复杂KC输入模式的激活 体内个体气味的激活足以有力地驱动MBON尖峰。在这里，我们将结合 用计算模型进行体内实验分析以确定MBON的控制机制 在基线条件下的激活和对记忆诱导的可塑性的响应。我们将确定 不同子集的粗壮MBON激活所需的最少数量的KCs和KC突触 在活体内接近和避免MBON。这些数据将指导计算方法来识别 KC-MBON相互作用的一般和特定特征。然后，结果将用于确定 体内特定腔室的塑性机制，以及相应的计算机制 硅胶。最后，我们将这些方法扩展到多个KC-MBON模块的同时分析，以 为决策提供计算依据。

Decision making largely depends on the integration of prior sensory experiences that are stored in the brain as memories. One of the best-understood model systems of a two-choice decision process is associative olfactory memory in Drosophila. In the fly, learning occurs at the synapse between Kenyon cells (KCs) and mushroom body output neurons (MBONs). By pairing olfactory stimuli with a reward or a punishment flies can reliably learn to distinguish two initially neutral odors. As in mammals, dopaminergic neurons (DANs) represents this contextual valence and modulate the strength or connectivity of KC>MBON synapses within distinct compartments of the MB, consequently altering behavior. To understand how this decision making is encoded in memory we thus must first understand the computation performed within individual DAN-KC-MBON modules. In preliminary work, we built a realistic computational model of the MBON-3 neuron including the precise synaptic connectivity all 948 innervating KCs based on the complete synaptic connectome of the MB. Our model incorporates precise membrane properties of the MBON-3 neuron that we obtained by performing patch-clamp recording in vivo. We demonstrate that MBON-3 is electrotonically compact and show that activation of complex KC input patterns reflecting physiological activation by individual odors in vivo are sufficient to robustly drive MBON spiking. Here, we will combine experimental analysis in vivo with computational modelling to determine the mechanisms controlling MBON activation under baseline conditions and in response to memory-induced plasticity. We will identify the minimal number of KCs and KC synapses required for robustMBON activation for different subsets of approach and avoidance MBONs in vivo. These data will guide computational approaches to identify general and specific features of the KC-MBON interactions. The results will then serve to identify compartment-specific plasticity mechanisms in vivo, and corresponding computational mechanisms in silico. Finally, we extend these approaches to simultaneous analyses at multiple KC-MBON modules to provide a computational basis for decision-making.