CRI: Neuronal Ensembles as Encoding and Processing Probability Density Functions

IBN：9634314 PI：John Clark 这项研究将寻求更深入地了解神经系统如何 表示和处理有关世界的信息，通过应用 信息论和统计推断的既定原则 在大量神经生物学观察的背景下。 的 要探索的基本假设是， 有意义的变量（例如视觉深度的测量，光流， 肢体定向等）被编码在一个集合的发射率中 的神经元。 贝叶斯推理然后提供了一个最佳的框架， 在存在不确定性的情况下，给定一定的先验知识， 这可能是由基因或学习过程决定的。 这种方法以一种自然的方式导致神经元回路， 比神经网络理论的标准输入输出图更一般。 例如，对实验的解释证明了上下文或 注意力对神经元反应的影响可以用涉及倍增性突触相互作用的网络来表示。 具体 研究的重点将包括（i）小规模神经元的研究 电路，说明了模型的计算能力，并提供 用于大规模模拟重要大脑子系统的原型， (ii)学习规则的结合，允许天真的电路， 修改其结构并优化任务性能。 跨学科 在本质上，该项目将加入计算的专业知识， 神经科学家和理论物理学家在工作中要执行 主要是在一位著名神经生理学家的实验室里。 该项目的核心是物理研究生参与一个 新的跨学科培训计划在计算神经科学。

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.