The neural mechanisms that lead to sparse coding within the midbrain of weakly electric fish

导致弱电鱼中脑稀疏编码的神经机制

• 批准号: RGPIN-2015-04198
• 负责人: Chacron, Maurice
• 金额: $ 2.91万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=647033
• 关键词: neural; mechanisms; lead; sparse; coding

Understanding how natural stimuli are encoded by peripheral sensory neurons and subsequently decoded by higher structures, the neural code, is a central problem in neuroscience. The long-term goals of the proposed research program are to understand the cellular and network mechanisms that make sensory neurons more selective in their responses to natural sensory input. It is generally agreed that peripheral sensory neurons employ dense neural codes (i.e. codes in which neurons respond to a wide range of stimuli) and that neurons in higher areas employ sparse neural codes (i.e. codes in which neurons respond selectively to the sparsely distributed informative components of natural stimuli). These sparse codes have now been widely observed across animal taxa, including the human hippocampus, monkey visual cortex, locust olfactory system, and songbird forebrain. It is argued that such sparse codes are more efficient because individual neurons detect specific features of sensory input and are metabolically less costly than dense codes. At the same time, recognition of a given stimulus feature (e.g. a particular object, face) requires that neural responses become robust to the sometimes very different inputs associated with this feature (e.g. the same object observed from different viewpoints). I propose to continue this research program by focusing on understanding the mechanisms that make midbrain neurons respond selectively to communication stimuli using the well-characterized electrosensory system of the gymnotiform fish Apteronotus leptorhynchus focusing on the following key issues: 1) Where are dense and sparse coding neurons located in TS? 2) What are the mechanisms that mediate the emergence of a sparse code and how are the tradeoffs between selectivity and feature invariance achieved? I propose to answer these important questions using a combination of in vivo patch clamp recordings, pharmacology, and mathematical modeling, all of which are routinely achieved in my laboratory. This combination has already successfully uncovered the mechanisms that lead to directional selectivity in midbrain neurons and I am confident that it will also successfully uncover the mechanisms that lead to responses selectivity and invariance to communication stimuli. In particular, we will test whether specific membrane conductances are critical in order to achieve response selectivity and feature invariance by using specific blockers as done previously. Further, using a combination of anatomical and physiological data, we will test whether the transformation from a dense code to a sparse code occurs sequentially in the electrosensory midbrain or whether dense and sparse codes co-exist in parallel. It is expected that the uncovered mechanisms will be generic and thus applicable to other systems and species. **

了解自然刺激如何被外周感觉神经元编码，并随后被更高级的结构（神经编码）解码，是神经科学的一个核心问题。该研究计划的长期目标是了解使感觉神经元在对自然感觉输入的反应中更具选择性的细胞和网络机制。人们普遍认为，外周感觉神经元使用密集的神经编码（即神经元对广泛的刺激作出反应的编码），而高级区域的神经元使用稀疏的神经编码（即神经元对自然刺激中稀疏分布的信息成分作出选择性反应的编码）。这些稀疏编码现已在动物分类群中被广泛观察到，包括人类的海马、猴子的视觉皮层、蝗虫的嗅觉系统和鸣禽的前脑。有人认为，这种稀疏编码更有效，因为单个神经元检测到感觉输入的特定特征，并且代谢成本低于密集编码。同时，识别一个给定的刺激特征（例如，一个特定的物体，人脸）需要神经反应对与该特征相关的有时非常不同的输入（例如，从不同的角度观察到的同一个物体）变得强大。我建议通过利用裸子鱼Apteronotus leptorhynchus具有良好特征的电感觉系统，重点了解中脑神经元选择性响应通信刺激的机制，从而继续研究该项目，重点关注以下关键问题：1)TS中密集和稀疏的编码神经元位于何处？2)稀疏代码出现的中介机制是什么？如何实现选择性和特征不变性之间的权衡？我建议使用体内膜片钳记录、药理学和数学建模的组合来回答这些重要的问题，所有这些都是在我的实验室常规实现的。这种结合已经成功地揭示了导致中脑神经元定向选择性的机制，我相信它也将成功地揭示导致对通信刺激的反应选择性和不变性的机制。特别是，我们将测试特定的膜电导是否至关重要，以便通过使用特定的阻滞剂实现响应选择性和特征不变性。此外，结合解剖和生理数据，我们将测试从密集代码到稀疏代码的转换是否在电感觉中脑中顺序发生，或者密集代码和稀疏代码是否并行共存。预计发现的机制将是通用的，从而适用于其他系统和物种。**