通过神经群体动力学进行计算。
Computation Through Neural Population Dynamics.
机构信息
Department of Bioengineering, Stanford University, Stanford, California 94305, USA; email:
Wu Tsai Neurosciences Institute, Stanford University, Stanford, California 94305, USA.
出版信息
Annu Rev Neurosci. 2020 Jul 8;43:249-275. doi: 10.1146/annurev-neuro-092619-094115.
Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.
大量的实验、计算和理论工作已经确定了相互连接的神经元群体协调活动中的丰富结构。现在面临的一个新挑战是揭示相关计算的本质、它们是如何实现的，以及它们在驱动行为中扮演什么角色。我们将其称为通过神经群体动力学进行计算。如果成功，这个框架将揭示神经群体活动的一般模式，并定量描述神经群体动力学如何实现驱动目标导向行为所需的计算。在这里，我们从动力系统理论的数学入门和将这一视角应用于实验数据所需的分析工具开始。接下来，我们重点介绍一些最近的发现，这些发现是成功应用动力系统的结果。我们关注的研究领域包括运动控制、定时、决策和工作记忆。最后，我们简要讨论了该神经群体动力学计算框架的一些有前途的近期研究方向和未来方向。
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