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Computation Through Neural Population Dynamics.
Annual Review of Neuroscience ( IF 12.1 ) Pub Date : 2020-07-08 , DOI: 10.1146/annurev-neuro-092619-094115
Saurabh Vyas 1, 2 , Matthew D Golub 2, 3 , David Sussillo 2, 3, 4 , Krishna V Shenoy 1, 2, 3, 5
Affiliation  

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.

中文翻译:


通过神经人口动态计算。

重要的实验、计算和理论工作已经在相互关联的神经群体的协调活动中确定了丰富的结构。现在一个新的挑战是揭示相关计算的性质、它们是如何实现的以及它们在驾驶行为中扮演什么角色。我们通过神经种群动态来命名这种计算。如果成功,该框架将揭示神经群体活动的一般主题,并定量描述神经群体动态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观点应用于实验数据所必需的分析工具开始。接下来,我们重点介绍动态系统的成功应用所产生的一些最新发现。我们专注于跨越运动控制、时间、决策和工作记忆的研究。最后,我们通过神经种群动力学框架简要讨论了最近有希望的研究方向和计算的未来方向。

更新日期:2020-07-09
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