CRCNS US-Israel Research Proposal: Understanding single neuron computation by combining biophysical and statistical models

是什么使神经元变得活跃？这个问题对于我们理解大脑过程至关重要，它既是一个关于潜在生物机制的生物物理学问题，也是一个关于神经元响应的传入刺激特征的统计学问题。该项目的总体目标是建立神经元活动的生物学机制与神经元编码传入刺激特征的计算过程之间的联系。 更具体地说，该项目旨在了解神经元刺激编码的生物学基础。神经元的动力学模型包含了这些细胞表达的离子通道的详细信息，提供了神经元活动的详细生物物理解释。这些类型的模型已经被广泛使用，并且可以包含和约束令人印象深刻的生物细节。 不幸的是，它们对神经元活动的意义或神经元正在执行的刺激的计算和转换的类型几乎没有提供任何见解。 另一方面，从统计方法导出的模型能够捕捉神经活动和神经元接收的刺激之间的通常嘈杂和复杂的关系。这些模型提供了对传入刺激的特定特征如何被神经元群体提取和组合的洞察。这些方法将通过卡内基梅隆大学（Urban和Kass）的一个团队和Bar-Ilan大学（Korngreen）的一个团队的合作结合起来，该团队具有将统计和生物物理模型应用于单神经元数据的专业知识。这项工作将集中在两种神经元类型上，它们具有几个重要的共同特征。 嗅球僧帽细胞和第5层新皮质锥体细胞是两类大型神经元，它们将不同来源的输入输入接收到其精心制作的树突树的不同部分上。为了建立动态和统计模型之间的这种联系，该项目将使用最近描述的方法开发详细的生物物理模型，并扩展当前统计模型的框架，以解释离子通道的功能后果及其在特定输入类别上的定位。应用这些改进的方法，并检查改变生物物理特性的神经元的能力，以鲁棒性和有效地表示刺激的后果将产生一个新的帐户的联系，因为生物机制和单神经元计算。以色列两国科学基金会（BSF）正在资助一个配套项目。

What make a neuron become active? This question, central to our understanding of brain processes, is both a biophysical question about underlying biological mechanisms, and a statistical question about the features of incoming stimuli to which a neuron responds. The overall goal of this project is to forge a link between the biological mechanisms of neuronal activity and the computational process by which neurons encode features of incoming stimuli. More specifically, this project seeks to understand the biological underpinnings of stimulus coding by neurons.Dynamical models of neurons that incorporate detailed information about the ion channels that these cells express provide a detailed, biophysical account of neuronal activity. These kinds of models have been used widely and can incorporate and constrain an impressive amount of biological detail. Unfortunately they provide little insight into the meaning of neuronal activity or into the kinds of computations and transformations of stimuli that neurons are performing. On the other hand, models derived from statistical approaches are able to capture the often-noisy and complex relationships between neural activity and the stimuli that a neuron receives. These models provide insight into how specific features of incoming stimuli are extracted and combined by populations of neurons. These approaches will be combined through collaboration of a team at Carnegie Mellon University (Urban and Kass) and one at Bar-Ilan University (Korngreen) with expertise in the application of statistical and biophysical models to single neuron data. The work will focus on two neuron types that have several important features in common. Olfactory bulb mitral cells and layer 5 neocortical pyramidal cells are two classes of large neurons that receive distinct sources of input inputs onto different divisions of their elaborate dendritic trees. To forge this connection between dynamic and statistical models, this project will develop detailed biophysical models using recently described methods and extend the framework of current statistical models to allow the interpretation of the functional consequences of ion channels and their localization on specific classes of inputs. Applying these improved methods, and examining the consequences of changing biophysical properties on the ability of neurons to robustly and effectively represent stimuli will generate a novel account of the linkage because biological mechanisms and single neuron computation. A companion project is being funded by the Israel Binational Science Foundation (BSF).