Dynamical systems, attractors, and neural circuits
Dynamical systems, attractors, and neural circuits
复制标题
动力系统、吸引子和神经回路
作者：
P. Miller
生物学是研究动力系统的学科。然而，我们大多数从事生物学工作的人在动力系统理论方面的教学训练有限，这是一个不幸的历史事实，可以为未来几代生命科学家弥补。在我的系统神经科学领域，神经回路在所有描述层次上都充满了非线性，使得简单的方法和我们自己的直觉变得不可靠。因此，我们的想法很可能是错误的，除非有好的模型提供信息。这些模型应该基于动力系统的数学理论，因为功能神经元是动态的它们随着时间改变它们的膜电位和放电率。因此，选择适当类型的动力系统作为模型的基础是建模过程中重要的第一步。这一步很容易出错，部分原因是有许多框架可供选择，部分原因是稀疏采样的数据可以与各种动态过程保持一致，部分原因是每个建模者都有一个难以摆脱的首选建模方法。这篇简短的综述总结了神经回路中可能出现的一些主要动力学范式，并评论了它们在计算上可以实现什么，以及哪些签名可能揭示它们在经验数据中的存在。我提供了使用两个或三个单元的简单电路的不同动力系统的示例，强调任何一种连接模式都与多种不同的功能兼容。
Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic—they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.
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影响因子：
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