Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, United States.
The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, United States.
Elife. 2020 May 20;9:e51473. doi: 10.7554/eLife.51473.
Competitive selection, the transformation of multiple competing sensory inputs and internal states into a unitary choice, is a fundamental component of animal behavior. Selection behaviors have been studied under several intersecting umbrellas including decision-making, action selection, perceptual categorization, and attentional selection. Neural correlates of these behaviors and computational models have been investigated extensively. However, specific, identifiable neural circuit mechanisms underlying the implementation of selection remain elusive. Here, we employ a first principles approach to map competitive selection explicitly onto neural circuit elements. We decompose selection into six computational primitives, identify demands that their execution places on neural circuit design, and propose a canonical neural circuit framework. The resulting framework has several links to neural literature, indicating its biological feasibility, and has several common elements with prominent computational models, suggesting its generality. We propose that this framework can help catalyze experimental discovery of the neural circuit underpinnings of competitive selection.
竞争选择，即将多个竞争的感觉输入和内部状态转化为单一选择的过程，是动物行为的一个基本组成部分。选择行为已经在几个相互交叉的领域进行了研究，包括决策、动作选择、感知分类和注意力选择。这些行为的神经关联和计算模型已经得到了广泛的研究。然而，选择实施的具体、可识别的神经回路机制仍然难以捉摸。在这里，我们采用一种基本原理的方法，将竞争选择明确地映射到神经回路元件上。我们将选择分解为六个计算基元，确定它们的执行对神经回路设计的要求，并提出一个规范的神经回路框架。由此产生的框架与神经文献有几个联系，表明其具有生物学可行性，并且与突出的计算模型有几个共同的元素，表明其具有通用性。我们提出，这个框架可以帮助促进对竞争选择的神经回路基础的实验发现。
