Goal-driven selective attention (GDSA) refers to the brain's function of prioritizing the activity of a task-relevant subset of its overall network to efficiently process relevant information while inhibiting the effects of distractions. Despite decades of research in neuroscience, a comprehensive understanding of GDSA is still lacking. We propose a novel framework using concepts and tools from control theory as well as insights and structures from neuroscience. Central to this framework is an information-processing hierarchy with two main components: selective inhibition of task-irrelevant activity and top-down recruitment of task-relevant activity. We analyze the internal dynamics of each layer of the hierarchy described as a network with linear-threshold dynamics and derive conditions on its structure to guarantee existence and uniqueness of equilibria, asymptotic stability, and boundedness of trajectories. We also provide mechanisms that enforce selective inhibition using the biologically inspired schemes of feedforward and feedback inhibition. Despite their differences, both lead to the same conclusion: the intrinsic dynamical properties of the (not-inhibited) task-relevant subnetworks are the sole determiner of the dynamical properties that are achievable under selective inhibition.

中文翻译：

---

线性阈值大脑网络中的分层选择性招募__art I：单层动力学和选择性抑制


目标驱动选择性注意（GDSA）是指大脑优先考虑整个网络中与任务相关的子集的活动的功能，以有效地处理相关信息，同时抑制分心的影响。尽管神经科学研究已经进行了数十年，但仍然缺乏对 GDSA 的全面理解。我们使用控制理论的概念和工具以及神经科学的见解和结构提出了一个新颖的框架。该框架的核心是一个信息处理层次结构，它有两个主要组成部分：选择性抑制与任务无关的活动和自上而下招募与任务相关的活动。我们分析了被描述为具有线性阈值动力学的网络的层次结构的每一层的内部动力学，并推导了其结构的条件，以保证平衡的存在和唯一性、渐近稳定性和轨迹的有界性。我们还提供了使用受生物学启发的前馈和反馈抑制方案强制执行选择性抑制的机制。尽管存在差异，但两者都得出相同的结论：（未抑制的）任务相关子网的内在动力学特性是选择性抑制下可实现的动力学特性的唯一决定因素。