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.

项目概要 在自然界中，感官知觉和运动处理是闭环运行的。自生运动影响 感觉输入，感觉输入指导未来的运动命令。通过经验，大脑可以学习 生成感觉运动模型形式的感觉输入和运动之间的相互关系 预测即将发生的行动的感官后果。在脊椎动物中，嗅觉与 通过嗅觉进行运动动作，就像其他感官方式一样，通过头部和身体运动。由于 然而，技术挑战，大多数实验室环境中的研究都探讨了嗅觉处理过程 被动气味采样。即使在研究气味驱动的导航时，运动对气味的影响 很少对答复进行分析。在这里，我们将测试中心假设，即在闭环嗅觉中， 小鼠对其行为的感官后果产生嗅觉运动预测，从而进一步指导气味 采样运动。在电路层面，我们假设特定的嗅觉皮层电路代表嗅觉 运动预测误差，通过比较气味输入和预期的运动相关预测来计算 气味输入。我们计划使用一种新颖的闭环气味定位任务（Smellocator）来测试这些假设 我们团队开发的，以及丰富的感觉运动扰动、最先进的录音 以及特异性不断提高的细胞型电路分析工具。 ● 为此，我们首先研究小鼠是否在运动和气味感知的闭环耦合下 检测嗅觉运动错误，并进一步补偿它们。在气味定位器任务中，头部固定的小鼠学会 用爪子操纵轻型杠杆，根据固定增益控制气味源的横向位置 感觉运动映射规则。在捕捉试验中，我们将暂时改变杠杆运动和 通过一系列精确的、意想不到的感觉运动扰动来消除气味。初步数据表明 专家小鼠成功计算出感觉运动预测错误，并快速进行精细纠正 这些扰动以个体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 面对瞬态意外时的运动预测错误。我们将检查是否存在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是否 不同的半月细胞和锥体细胞类型（例如 Netrin+、Cux1+、Tbr1+、Tle4+）主要代表感觉 通过结合分布式记录和现代基因标记策略来比较输入与嗅觉运动错误。 • 最后，我们将研究嗅觉皮层是否能够在存在持续嗅觉的情况下进行适应。 电机错误。我们将在试验块和行为会话中改变感觉运动映射规则， 并比较特定细胞类型在利用灵活的支持感觉运动适应方面的作用 光遗传学抑制策略。