CAREER: Dendritic Subunits in Cortical Visual Processing and Development

职业：皮质视觉处理和发育中的树突状亚基

• 批准号: 9734350
• 负责人: Bartlett Mel
• 金额: $ 33.63万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-01 至 2003-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9734350&HistoricalAwards=false
• 关键词: CAREER; Dendritic; Subunits; Cortical; Visual

IBN 97-34350 MEL Neurons, the computing cells of the brain, typically possess extensive, highly branched tree-like structures called dendrites onto which tens of thousands of input connections from other neurons are formed. In contrast to the classical conception that the brain's computation power lies primarily in the "neural network", i.e. in the richly interconnected network of nerve cells, evolving understanding over the last 40 years and including modeling work in Dr. Mel's laboratory, among several others, has established that computations carried out within the dendritic trees of individual neurons can contribute in significant ways to the computing functions of neural tissue. One of the important principles to emerge from this modeling work is that different computations may be carried out simultaneously in different "subunits" of the dendritic tree, which could dramatically increase the computational capacities of individual nerve cells. A second principle is that the learning and memory may involve changes not only in the absolute potency of cell-to-cell connectivity but in the spatial arrangements of input connections onto the dendrites of individual neurons as well. Whether parallel subunit computations actually occur within the dendrites of real neurons remains to be determined. However, the design of experiments to answer these questions is complicated by the fact that the measured physiological responses of neurons can be due to either to internal operations of individual cells or the combined effects of networks of cells. In this work, Dr. Mel and his colleagues will model the roles and interaction of single- neuron computations and those carried out by the embedded network of interconnected neurons, in order to guide experimental approaches relating to critical questions of neural processing.

IBN 97-34350 MEL 神经元是大脑的计算细胞，通常具有广泛的、高度分支的树状结构，称为树突，在树突上形成了数万个来自其他神经元的输入连接。 与传统观念相反，大脑的计算能力主要在于“神经网络”，即神经细胞的丰富互连网络，在过去40年中不断发展的理解，包括Mel博士实验室的建模工作，以及其他几个，已经确定，在单个神经元的树突树内进行的计算可以以重要的方式对神经组织的计算功能做出贡献。 从这种建模工作中出现的一个重要原则是，不同的计算可以在树突树的不同“子单元”中同时进行，这可以显着增加单个神经细胞的计算能力。 第二个原理是，学习和记忆可能不仅涉及细胞间连接的绝对效能的变化，还涉及单个神经元树突上输入连接的空间排列的变化。 并行子单元计算是否真的发生在真实的神经元的树突内还有待确定。 然而，回答这些问题的实验设计是复杂的，因为测量的神经元生理反应可能是由于单个细胞的内部操作或细胞网络的组合效应。 在这项工作中，Mel博士和他的同事将对单神经元计算的作用和相互作用以及由互连神经元的嵌入式网络进行的计算进行建模，以指导与神经处理关键问题相关的实验方法。