“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中的先前感觉经验的整合。 大脑作为记忆。二选一决策过程的最佳理解模型系统之一是 果蝇的嗅觉联想记忆在果蝇中，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 惩罚蝇可以可靠地学习区分两种最初中性的气味。在哺乳动物中，多巴胺能 神经元（DAN）代表这种背景效价，并调节KC>MBON的强度或连接性 在MB的不同隔室内的突触，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。在初步工作中，我们建立了一个现实的计算模型， MBON-3神经元包括精确的突触连接，所有948个神经支配的KC基于完整的 MB的突触连接体。我们的模型采用了精确的膜性能的MBON-3 我们通过在体膜片钳记录获得的神经元。我们证明MBON-3是 电紧张性紧凑，并显示复杂的KC输入模式的激活，反映了生理 在体内通过单独的气味激活足以稳健地驱动MBON尖峰。在这里，我们将联合收割机 通过计算建模进行体内实验分析，以确定控制MBON的机制 在基线条件下激活和响应记忆诱导的可塑性。我们将确定 最小数量的KC和KC突触所需的robustMBON激活的不同子集， 接近和避免体内MBON。这些数据将指导计算方法来识别 KC-MBON相互作用的一般和具体特征。结果将用于识别 在体内的特定区室的可塑性机制，以及相应的计算机制， silico。最后，我们将这些方法扩展到多个KC-MBON模块的同时分析， 为决策提供计算基础。

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.