RI: Small: Theory of Robust Learning Based on the Structure and Function of the Cortical Column

RI：小：基于皮质柱结构和功能的鲁棒学习理论

• 批准号: 1526642
• 负责人: Armen Stepanyants
• 金额: $ 16.76万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526642&HistoricalAwards=false
• 关键词: RI; Small; Theory; Robust; Learning

How the brain learns, and in the process modifies its synaptic connectivity, remains one of the greatest mysteries of modern science. The objective of this project is to uncover the effects of robust associative learning and long-term memory storage on synaptic connectivity, thus creating the basis for quantitative analyses of these fundamental brain functions. The investigator proposes to develop a biologically realistic model of robust associative learning by cortical circuits. The model will be derived from a single hypothesis, according to which synaptic connectivity in a given circuit of adult cortex is functioning in a steady-state. In such a state the associative memory storage capacity of the circuit is maximal, and learning new associations is accompanied with forgetting some of the old ones. The model will integrate current knowledge of excitatory and inhibitory neuron classes, with structural connectivity constraints imposed by the morphologies of axonal and dendritic arbors of cortical neurons, with homeostatic constraints on numbers and strengths of synaptic connections. It is proposed to simulate steady-state learning based on one of the best studied networks in the mammalian neocortex - the barrel-centered column of rodent somatosensory cortex. The simulations will be imbedded in the structural connectivity of the column, built from the morphologies of neurons reconstructed in three-dimensions from various cortical depths. Salient features of steady-state circuits will be validated against a large dataset of experimental studies reporting probabilities of connections between neurons, probabilities of specific higher-order connectivity motifs, distributions of unitary postsynaptic potentials, as well as relative strengths of laminar and inter-laminar projections in rodent barrel cortex. The dataset will be created as part of the project and will encompass connectivity of major excitatory and inhibitory cell classes present in all cortical layers. The proposed research is rooted in the basic principles of statistical learning and will advance the state of the art in theoretical and computational modeling of cognitive functions with basic neuroscience and computational intelligence applications.

大脑是如何学习的，并在这个过程中改变其突触连接，这仍然是现代科学最大的谜团之一。该项目的目标是揭示强健的联想学习和长期记忆存储对突触连接的影响，从而为定量分析这些基本的大脑功能奠定基础。这位研究人员建议开发一种通过大脑皮层回路进行健壮联想学习的生物现实模型。这个模型将从一个单一的假设中得出，根据这个假设，给定的成人大脑皮质回路中的突触连接是在稳定状态下运行的。在这种状态下，电路的联想记忆存储容量最大，学习新的联想伴随着忘记一些旧的联想。该模型将整合兴奋性和抑制性神经元类别的现有知识，与皮质神经元轴突和树突的形态施加的结构连通性限制，以及对突触连接数量和强度的稳态限制。有人建议基于哺乳动物新大脑皮层中研究最多的网络之一-啮齿动物体感皮层的桶中心柱来模拟稳态学习。这些模拟将被嵌入到柱子的结构连接性中，该柱子基于从不同皮质深度重建的三维神经元的形态。稳态电路的显著特征将通过大量实验研究来验证，这些研究报告了神经元之间连接的概率、特定高阶连接基元的概率、单位突触后电位的分布，以及啮齿动物桶状皮质中层和层间投射的相对强度。数据集将作为项目的一部分创建，并将包括存在于所有皮质层的主要兴奋性和抑制性细胞类别的连通性。这项拟议的研究植根于统计学习的基本原理，并将通过基础神经科学和计算智能应用促进认知功能的理论和计算建模方面的最先进水平。