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

了解自然刺激如何被外周感觉神经元编码，然后被更高的结构（神经密码）解码，是神经科学的核心问题。拟议的研究计划的长期目标是了解细胞和网络机制，使感觉神经元对自然感觉输入的反应更具选择性。人们普遍认为，外周感觉神经元采用密集的神经代码（即神经元对大范围刺激做出反应的代码），而更高区域的神经元采用稀疏的神经代码（即神经元选择性地对自然刺激中稀疏分布的信息成分做出反应的代码）。这些稀疏编码现在已经在动物分类群中被广泛观察到，包括人类海马体，猴子视觉皮层，蝗虫嗅觉系统和鸣禽前脑。有人认为，这种稀疏的代码是更有效的，因为单个神经元检测到的感官输入的特定功能，代谢成本低于密集的代码。同时，给定刺激特征（例如，特定对象、面部）的识别要求神经响应对于与该特征相关联的有时非常不同的输入（例如，从不同视角观察到的相同对象）变得鲁棒。我建议继续这项研究计划，重点是了解机制，使中脑神经元选择性地响应通信刺激使用的特点良好的电感觉系统的裸形鱼Apteronotus leptorhynchus集中在以下关键问题：1）密集和稀疏的编码神经元位于TS？2)是什么机制促成了稀疏代码的出现？如何在选择性和特征不变性之间进行权衡？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问题，所有这些都是在我的实验室中常规实现的。这种结合已经成功地揭示了导致中脑神经元方向选择性的机制，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是至关重要的，以实现响应选择性和功能不变性，通过使用特定的阻断剂，如前所述。此外，使用解剖和生理数据的组合，我们将测试是否从密集的代码到稀疏的代码的转换顺序发生在电感觉中脑或密集和稀疏的代码是否并行共存。预计发现的机制将是通用的，因此适用于其他系统和物种。

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.