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

大脑如何学习，并在此过程中修改其突触连接，仍然是现代科学中最大的谜团之一。该项目的目标是揭示强大的联想学习和长期记忆存储对突触连接的影响，从而为这些基本脑功能的定量分析奠定基础。研究者建议开发一个生物现实的模型，强大的联想学习皮层电路。该模型将来自一个单一的假设，根据该假设，在一个给定的成年人皮层回路的突触连接是在一个稳定的状态下运作。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的现有知识，与皮层神经元轴突和树突的形态所施加的结构连接约束，与突触连接的数量和强度的稳态约束。有人建议模拟稳态学习的基础上，在哺乳动物的新皮层的最好的研究网络之一-啮齿动物的躯体感觉皮层的桶为中心的列。这些模拟将嵌入到柱的结构连接中，该柱是由从不同皮层深度三维重建的神经元形态构建的。稳态电路的显着特点将被验证对一个大的实验研究数据集，报告神经元之间的连接的概率，特定的高阶连接图案的概率，单一突触后电位的分布，以及相对强度的层状和层间的预测在啮齿动物桶皮质。该数据集将作为项目的一部分创建，并将包括所有皮质层中存在的主要兴奋性和抑制性细胞类别的连接。拟议的研究植根于统计学习的基本原理，并将推进认知功能的理论和计算建模与基础神经科学和计算智能应用的最新技术。

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.