Attractors and Criticality in Cortical Slice Cultures

物理学在描述复杂现象如何从许多相似单位的集体相互作用中出现方面取得了巨大的成功。波、同步、相变和自组织都是这样的例子。尽管大脑非常复杂，但它是由许多单位、神经元组成的，这些单位看起来很相似。这种相似性导致许多研究人员借用物理学的概念来解释神经功能。仿真表明，稳定状态可以用来存储信息，并且临界点使信息传输和信息存储最大化。尽管这一理论体系蓬勃发展，但对其进行检验的实验却寥寥无几。新的技术进步使数以千计的相互连接的神经元能够在由许多电极组成的微型制造阵列上进行培养。这些培养的脑片可以存活数周，同时记录它们的自发电活动。这些实验产生的大量数据集使许多由统计物理学启发的假说可以在真实的神经组织中进行检验。为了推进这项研究，提出了一系列实验来检验关于稳定状态和临界点的假设。这些实验的数据将被用来构建补充的网络模拟，这些模拟应该会导致进一步的可测试预测。从这些实验中获得的知识有望促进对活神经元网络所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。这些研究预计将在两个领域产生更广泛的影响。首先，为这些培养实验开发的分析工具预计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，他们有望在解释海量神经数据方面发挥重要作用，这些数据肯定会随着记录技术和计算机能力的不断提高而到来。

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.