CRI: Neuronal Ensembles as Encoding and Processing Probability Density Functions

IBN：9634314 PI：John Clark这项研究将通过在大量神经生物学观察的背景下应用信息论和统计推理的既定原则，寻求对神经系统如何表示和处理关于世界的信息的更深层次的理解。要探索的基本假设是关于有意义变量的概率信息(如视觉深度、光流、肢体方向等的测量)。编码在一组神经元的放电率中。在给定某些先验知识的情况下，贝叶斯推理提供了在存在不确定性的情况下进行推理的最佳框架，这些先验知识可能是通过遗传或通过学习过程确定的。这种方法以一种自然的方式导致了神经元电路，在计算上比神经网络理论的标准输入-输出映射更通用。例如，对实验表明背景或注意对神经元反应的影响的解释可以用涉及乘性突触相互作用的网络来表述。这项研究的具体内容将包括：(I)研究小型神经元电路，以说明模型的计算能力，并为重要大脑子系统的大规模模拟提供原型；(Ii)纳入学习规则，允许朴素的电路修改其结构并优化任务性能。该项目本质上是跨学科的，将加入一名计算神经科学家和一名理论物理学家的专业知识，主要在一名著名神经生理学家的实验室进行工作。该项目的核心是让物理学研究生参与一个新的计算神经科学跨学科培训计划。

IBN: 9634314 PI: John Clark The research will seek a deeper understanding of how neural systems represent and process information about the world, by applying established principles of information theory and statistical inference in the context of a large body of neurobiological observations. The basic hypothesis to be explored is that probabilistic information about meaningful variables (such as measures of visual depth, optical flow, limb orientation, etc.) is encoded in the firing rates of an ensemble of neurons. Bayesian inference then provides an optimal framework for inference in the presence of uncertainty, given certain prior knowledge that may be determined genetically or through learning processes. This approach leads in a natural way to neuronal circuits that are computationally more general than the standard input-output maps of neural-network theory. For example, an explanation of experiments demonstrating contextual or attentional influences on neuronal responses may be formulated in terms of a network involving multiplicative synaptic interactions. Specific thrusts of the research will include (i) the study of small-scale neuronal circuits that illustrate the computational power of the model and furnish prototypes for large-scale simulation of important brain subsystems and (ii) the incorporation of learning rules that permit naive circuits to modify their structure and optimize task performance. Cross-disciplinary in nature, the project will join the expertise of a computational neuroscientist and a theoretical physicist in work to be performed primarily in the laboratory of a prominent neurophysiologist. Central to the project is the involvement of physics graduate students in a new interdisciplinary training program in computational neuroscience.