Attractors and Criticality in Cortical Slice Cultures

皮质切片培养中的吸引子和临界性

• 批准号: 0343636
• 负责人: John Beggs
• 金额: $ 34.68万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-10-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0343636&HistoricalAwards=false
• 关键词: Attractors; Criticality; Cortical; Slice; Cultures

The physical sciences have had great success in describing how complex phenomena can emerge from the collective interactions of many similar units. Waves, synchrony, phase transitions, and self-organization are all examples of this. Although the brain is tremendously complex, it is composed of many units, neurons, which appear to be similar. This resemblance has led many researchers to borrow concepts from physics in an effort to explain neural function. Simulations indicate that stable states can be used to store information, and that the critical point maximizes both information transmission and information storage. While this body of theory has prospered, experiments to test it have been few. New advances in technology have allowed thousands of interconnected neurons to be grown in culture on microfabricated arrays of many electrodes. These cultured brain slices can be kept alive for weeks while their spontaneous electrical activity is recorded. The large data sets produced by these experiments allow many of the hypotheses inspired by statistical physics to be examined in real neural tissue. A series of experiments to test hypotheses about stable states and the critical point are proposed to advance this research. The data from these experiments will be used to construct complementary network simulations that should to lead to further testable predictions. Knowledge gained from these experiments is expected to advance the understanding of computational principles used by networks of living neurons and to be useful in designing synthetic devices that exploit these principles. These studies are expected to have broader impact in two areas. First, analysis tools developed for these culture experiments are expected to be applicable to data from whole behaving animals as well. Second, this research will contribute to training interdisciplinary scientists who are expected to be valuable in interpreting the deluge of neural data that will certainly come as recording technology and computer capacity continue to improve.

物理科学在描述了许多类似单位的集体互动中如何出现复杂现象在描述如何出现复杂的现象方面取得了巨大成功。波浪，同步，相变和自组织都是这样的例子。尽管大脑非常复杂，但它由许多单位，神经元组成，这些神经元似乎相似。这种相似之处使许多研究人员从物理学中借用概念，以解释神经功能。模拟表明稳定状态可用于存储信息，并且关键点可以最大化信息传输和信息存储。尽管这种理论繁荣了，但测试的实验很少。技术的新进步使数千个相互连接的神经元可以在许多电极的微生物阵列中生长。这些培养的​​大脑切片可以存活数周，同时记录它们的自发性电活动。这些实验产生的大数据集允许在实际神经组织中检查受统计物理学启发的许多假设。提出了一系列测试有关稳定状态和临界点的假设的实验，以推进这项研究。这些实验的数据将用于构建互补的网络模拟，以导致进一步的可测试预测。 从这些实验中获得的知识有望提高对活神经元网络使用的计算原理的理解，并有助于设计利用这些原理的合成设备。预计这些研究将在两个领域产生更大的影响。首先，预计为这些文化实验开发的分析工具也适用于整个行为动物的数据。其次，这项研究将有助于培训跨学科的科学家，他们有望在解释造成的神经数据中有价值，这些神经数据肯定会随着记录技术和计算机的能力不断改善而产生。