Attractors and Criticality in Cortical Slice Cultures

物理科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产生方面取得了巨大的成功。波、同步、相变和自组织都是这方面的例子。尽管大脑极其复杂，但它是由许多单元组成的，神经元，它们看起来是相似的。这种相似性使得许多研究人员从物理学中借用概念来解释神经功能。仿真结果表明，该方法可以采用稳定状态来存储信息，且临界点使信息传输和信息存储最大化。虽然这一理论体系蓬勃发展，但检验它的实验却很少。新技术的进步使得成千上万的相互连接的神经元可以在许多电极的微制造阵列上培养。这些培养的大脑切片可以存活数周，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统计物理学启发的许多假设能够在真实的神经组织中得到检验。提出了一系列实验来验证稳态和临界点的假设，以推进这一研究。这些实验的数据将用于构建互补的网络模拟，从而得出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来自完整行为动物的数据。其次，随着记录技术和计算机能力的不断提高，这项研究将有助于培养跨学科的科学家，他们有望在解释大量神经数据方面发挥重要作用。

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.