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？这个问题是我们理解大脑过程的核心，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对传入刺激作出反应的特征的统计学问题。该项目的总体目标是在神经元活动的生物学机制和神经元对传入刺激特征进行编码的计算过程之间建立联系。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，为神经元活动提供了详细的生物物理描述。这些类型的模型已经被广泛使用，并且可以包含和限制大量的生物细节。不幸的是，它们对神经元活动的意义以及神经元对刺激的计算和转换提供的见解很少。另一方面，基于统计方法的模型能够捕捉到神经活动和神经元接收到的刺激之间的复杂关系。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见解。这些方法将通过卡内基梅隆大学（Urban和Kass）和巴伊兰大学（Korngreen）的一个团队的合作结合起来，他们在统计和生物物理模型应用于单个神经元数据方面具有专业知识。这项工作将集中在两种神经元类型上，它们有几个重要的共同特征。嗅球二尖瓣细胞和第5层新皮层锥体细胞是两类大型神经元，它们接受不同来源的输入到它们精致的树突树的不同分支上。为了建立动态模型和统计模型之间的联系，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，以允许解释离子通道的功能后果及其在特定类别输入上的定位。应用这些改进的方法，并检查改变生物物理特性对神经元鲁棒和有效表征刺激能力的影响，将产生一种新的联系，因为生物机制和单神经元计算。以色列两国科学基金会（BSF）正在资助一个伙伴项目。

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).