Understanding the neuronal mechanisms of closed-loop olfaction

了解闭环嗅觉的神经机制

• 批准号: 10578521
• 负责人: Dinu Florentin ALBEANU
• 金额: $ 55.18万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-22 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10578521
• 关键词: Adaptive Behaviors; Anterior; Auditory; Behavioral; Brain; Code; Coupling; Data; Environment; Esthesia; Face; Feeling; Fiber; Future; Genetic; Head; Head Movements; Individual; Investigation; Knowledge; Label; Laboratories; Lateral; Learning; Link; Location; Modality; Modeling; Modernization; Monitor; Motor; Movement; Mus; Nature; Neurons; Odors; Olfactory Cortex; Olfactory Pathways; Perception; Photometry; Process; Pyramidal Cells; Reporting; Rewards; Role; Sampling; Sensory; Signal Transduction; Smell Perception; Source; Specificity; Stimulus; Techniques; Testing; Update; Vertebrates; Visual; cell type; expectation; experience; experimental study; flexibility; genetic signature; insight; light weight; neural circuit; novel; olfactory nuclei; optogenetics; predictive modeling; relating to nervous system; response; sensory input; somatosensory; tool

Project Summary In nature, sensory perception and motor processing operate in closed-loop. Self-generated movements impact sensory input, and sensory inputs guide future motor commands. Through experience, the brain may learn the reciprocal relationship between sensory inputs and movements in the form of generative sensorimotor models that predict the sensory consequences of upcoming actions. In vertebrates, olfaction is intrinsically linked to motor action through sniffing, and just as for other sensory modalities, via head and body movements. Due to technical challenges, however, most studies in laboratory settings have probed olfactory processing during passive odor sampling. Even when investigating odor-driven navigation, the effect of movements on odor responses has rarely been analyzed. Here we will test the central hypothesis that, in closed-loop olfaction, mice generate olfacto-motor predictions on the sensory consequences of their actions, which further guide odor sampling movements. At the circuit level, we hypothesize that specific olfactory cortex circuits represent olfacto- motor prediction errors, computed by comparing odor input and movement-related predictions of the expected odor input. We plan to test these hypotheses using a novel closed-loop odor localization task (Smellocator) developed in our group, together with a rich repertoire of sensorimotor perturbations, state-of-the-art recordings and cell-type circuit analysis tools with increasing levels of specificity. ● To this end, we will first investigate whether under closed-loop coupling of movements and odor sensing, mice detect olfacto-motor errors, and further compensate for them. In the Smellocator task, head-fixed mice learn to steer a lightweight lever with their paws to control the lateral location of an odor source according to a fixed-gain sensorimotor mapping rule. In catch trials, we will transiently alter the relationship between lever movement and odor displacement via a range of precise, unexpected sensorimotor perturbations. Preliminary data indicate that expert mice successfully compute sensorimotor prediction errors, and quickly engage in fine corrective movements triggered by these perturbations in an individual specific manner. • Then, we will investigate whether the olfactory cortex (piriform vs. anterior olfactory nucleus) represents olfacto- motor prediction errors in face of transient surprises. We will check whether brief sensorimotor perturbations trigger sudden changes in cortical activity (mismatch responses). We will refine our analysis to determine if different semilunar and pyramidal cells types (e.g. Netrin+, Cux1+, Tbr1+, Tle4+) represent primarily sensory inputs vs. olfacto-motor errors by combining distributed recordings and modern genetic labeling strategies. • Finally, we will investigate whether the olfactory cortex enables adaptation in the presence of persistent olfacto- motor errors. We will change the sensorimotor mapping rules in blocks of trials, and across behavioral sessions, and compare the roles of specific cell types in supporting sensorimotor adaptation taking advantage of flexible optogenetic suppression strategies.

项目摘要 在自然界中，感官知觉和运动处理在闭环中运行。自发运动影响 感觉输入和感觉输入指导未来的运动指令。通过经验，大脑可以学习 感觉输入和运动之间的相互关系，以生成感觉运动模型的形式 预测即将发生的行为的感官后果。在脊椎动物中，嗅觉与 通过嗅闻的运动动作，以及就像其他感官形式一样，通过头部和身体运动。由于 然而，技术上的挑战，大多数实验室环境中的研究都探讨了嗅觉加工过程中， 被动气味采样即使在研究气味驱动的导航时， 回答很少被分析。在这里，我们将测试中心假设，在闭环嗅觉， 老鼠产生嗅觉运动预测其行为的感官后果，这进一步指导气味 抽样运动。在回路水平，我们假设特定的嗅觉皮层回路代表嗅觉- 运动预测误差，通过比较气味输入和运动相关的预测， 气味输入。我们计划使用一种新的闭环气味定位任务（Smellocator）来测试这些假设 在我们的团队中发展，加上丰富的感觉运动扰动，最先进的录音， 和细胞类型的电路分析工具，具有越来越高的特异性水平。 ●为此，我们将首先研究在运动和气味感知的闭环耦合下，小鼠是否 检测嗅觉运动错误，并进一步补偿它们。在Smellocator任务中，头部固定的老鼠学会了 用它们的爪子操纵一个轻量级的杠杆，根据固定增益控制气味源的横向位置， 感觉运动映射规则在接球试验中，我们将暂时改变杠杆运动和 气味位移通过一系列精确的，意想不到的感觉运动扰动。初步数据表明 专家小鼠成功地计算感觉运动预测错误，并迅速进行精细的纠正， 这些扰动以个人特定的方式引发的运动。 ·然后，我们将研究嗅觉皮层（梨状核与前嗅核）是否代表嗅觉。 电机预测错误的瞬间惊喜。我们将检查短暂的感觉运动扰动 触发皮质活动的突然变化（不匹配反应）。我们将完善我们的分析，以确定 不同的半月形和锥体细胞类型（例如Netrin+，Cux 1+，Tbr 1+，Tle 4+）主要代表感觉 输入与嗅觉运动错误相结合的分布式记录和现代遗传标记策略。 ·最后，我们将研究嗅觉皮层是否能够在持续性嗅觉障碍存在的情况下进行适应。 运动错误我们将在一系列试验中改变感觉运动映射规则，在整个行为过程中， 并比较特定细胞类型在支持感觉运动适应中的作用， 光遗传学抑制策略。