动态自适应计算：根据任务需求调整网络状态
Dynamic Adaptive Computation: Tuning network states to task requirements
摘要：
神经电路能够在非常不同的条件和要求下执行计算。所需的计算对它们的微调施加了明确的约束：对刺激的快速和最大信息量的反应通常需要去相关的基线神经活动。这种网络动态被称为不规则网络。相反，信息的时空整合需要在延长的时间段内维持和传递刺激信息。这可以在临界状态下实现，临界状态是相关性、灵敏度和积分时间发散的相变。能够灵活地切换，甚至以任务相关的方式组合上述属性将呈现明显的功能优势。我们建议，皮层在“混响制度”，因为它是特别有利的准备适应上下文和任务的计算属性。这种混响机制使皮层网络能够通过有效突触强度或兴奋-抑制比的微小变化在不规则和临界状态之间进行内插。这些变化直接适应计算特性，包括灵敏度，放大，积分时间和相关长度在本地网络。我们回顾最近的证据表明，在体内的混响制度，皮层操作，以及各种皮层区域已经适应了他们的整合时间处理要求。此外，我们提出，神经调节使网络的微调，使本地电路可以去相关或整合，淬火或保持其输入取决于任务。我们认为，这种任务相关的调整，我们称之为“动态自适应计算”，提出了一个中央组织原则的皮层网络和讨论的第一个实验证据。
英文摘要：
Neural circuits are able to perform computations under very diverse conditions and requirements. The required computations impose clear constraints on their fine-tuning: a rapid and maximally informative response to stimuli in general requires decorrelated baseline neural activity. Such network dynamics is known as asynchronous-irregular. In contrast, spatio-temporal integration of information requires maintenance and transfer of stimulus information over extended time periods. This can be realized at criticality, a phase transition where correlations, sensitivity and integration time diverge. Being able to flexibly switch, or even combine the above properties in a task-dependent manner would present a clear functional advantage. We propose that cortex operates in a "reverberating regime" because it is particularly favorable for ready adaptation of computational properties to context and task. This reverberating regime enables cortical networks to interpolate between the asynchronous-irregular and the critical state by small changes in effective synaptic strength or excitation-inhibition ratio. These changes directly adapt computational properties, including sensitivity, amplification, integration time and correlation length within the local network. We review recent converging evidence that cortex in vivo operates in the reverberating regime, and that various cortical areas have adapted their integration times to processing requirements. In addition, we propose that neuromodulation enables a fine-tuning of the network, so that local circuits can either decorrelate or integrate, and quench or maintain their input depending on task. We argue that this task-dependent tuning, which we call "dynamic adaptive computation", presents a central organization principle of cortical networks and discuss first experimental evidence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
