CRI: Reliable and Adaptive Behavior of Neurons: Experiment and Theory

IBN：9634405 PI：阿巴巴内尔：在过去的十年里，旨在了解大脑的计算研究的数量大幅增加。这在很大程度上是计算机能力巨大扩张的结果，也是有希望的新程序的结果，这些程序有助于研究类似神经的现象，如模式识别和记忆形成。在此期间，我们在细胞和电路水平上对小神经系统的了解也出现了平行的增长。由于大脑在细胞数量和它们之间的连接方面是如此复杂，较小的无脊椎动物神经系统的运作方式与较大的神经系统完全相同，可以作为理解信息处理的基本属性的模型。现在有许多小电路已经完全用突触连接来描述了。虽然这样的电路可以从现象上建模，但对它们运行的基本原理缺乏深入的理解。我们的方法将是在龙虾的30个细胞的口胃神经系统中寻找这样的一般原理。我们将使用在物理学上非常成功的新的数学技术，对神经节产生的有节奏的电机输出模式进行动态的、非线性的分析。这些方法还没有应用于单一识别神经元和小神经回路水平上的神经系统研究。这个项目既特殊又及时，因为它具有在理论和实验之间来回移动的能力，分辨率在其他系统中是不可能的。通过这种方法，我们希望为神经网络如何产生和处理信息，从而为动物实现可靠、健壮和自适应的行为提供一种新的、可推广的描述。我们相信，像我们在这里描述的这样的跨学科协同，是机器人学和人工智能未来发展的关键，并提供关于神经处理的基本信息。

IBN: 9634405 PI: Abarbanel The last ten years has seen a huge increase in the number of computational studies aimed at understanding the brain. This is largely the result of an enormous expansion of computer power as well as promising new programs which are useful in studying neural-like phenomena such as pattern recognition and memory formation. During this period there has also been a parallel increase in our knowledge of small nervous systems at the cellular and circuit level. Because the brain is so complex in terms of the number of cells and connections between them, smaller invertebrate nervous systems, which operate in exactly the same manner as larger ones, can serve as models for understanding fundamental properties of information processing. There are now many small circuits which have been completely described in terms of synaptic connectivity. While such circuits can be modeled phenomenologically, there is a lack of deep understanding of the fundamental principles by which they operate. Our approach will be to look for such general principles in the thirty celled stomatogastric nervous system of the lobster. We will use new mathematical techniques which have been so successful in physics, to provide a dynamical, nonlinear analysis of the rhythmic motor output patterns generated by the ganglion. These methods have not yet been applied to studies of the nervous system at the level of single identified neurons and small neural circuits. This project is both special and timely because it has the ability to move back and forth between theory and experiment at a level of resolution not possible in other systems. Using this approach, we hope to provide a new and generalizable description of how neural networks produce and process information so as to achieve reliable, robust and adaptive behavior for the animal. We believe a cross-disciplinary synergy such as we describe here, holds the key for future developments in robotics and artificial intelligence as well as providin g basic information about neural processing.