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

–CRCNS：果蝇基于记忆的决策的计算原理 –

基本信息

批准号：
10456950
负责人：
ERNST NIEBUR
金额：
$ 19.77万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10456950
关键词：
Adaptive Behaviors Address Affect Anatomy Architecture Behavior Behavior Control Behavioral Behavioral Model Biological Models Brain Cells Cognition Complex Computer Models Computer Simulation Data Decision Making Development Disease Drosophila genus Equilibrium Frequencies Health Human Image Individual Investigation Lead Learning Mammals Mediating Membrane Memory Mental disorders Modeling Molecular Mushroom Bodies Nervous system structure Neurons Odors Organism Output Pattern Physiological Process Property Psyche structure Punishment Research Rewards Sensory Signal Transduction Smell Perception Synapses Testing Trees Work base behavior change behavior influence computational basis connectome dopaminergic neuron experience experimental analysis experimental study fly imaging modality in silico in vivo information processing insight interest long term memory memory process neural circuit novel olfactory stimulus optical imaging patch clamp postsynaptic reconstruction repaired response simulation two-photon

项目摘要

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.

决策很大程度上取决于存储在大脑中的先前感官经验的整合大脑作为记忆。二项选择决策过程中最容易理解的模型系统之一是果蝇的联想嗅觉记忆。在飞行中，学习发生在凯尼恩细胞之间的突触处（KC）和蘑菇体输出神经元（MBON）。通过将嗅觉刺激与奖励或惩罚果蝇能够可靠地学会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) 代表这种上下文效价并调节 KC>MBON 的强度或连接性 MB 不同区域内的突触，从而改变行为。要了解这是如何决策被编码在内存中，因此我们必须首先了解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 MBON-3 神经元包括基于完整的 948 个神经支配 KC 的精确突触连接 MB 的突触连接组。我们的模型融合了 MBON-3 的精确膜特性我们通过体内膜片钳记录获得的神经元。我们证明 MBON-3 是电紧张并表明复杂的 KC 输入模式的激活反映了生理学体内个体气味的激活足以强有力地驱动 MBON 尖峰。在这里，我们将结合通过计算模型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并响应记忆诱导的可塑性。我们将确定不同子集的稳健MBON激活所需的KC和KC突触的最小数量体内接近和避免MBON。这些数据将指导计算方法来识别 KC-MBON 相互作用的一般和具体特征。结果将用于确定体内隔室特异性可塑性机制，以及相应的计算机制硅片。最后，我们将这些方法扩展到多个 KC-MBON 模块的同步分析为决策提供计算基础。