在C语言中发现稀疏控制策略。elegans
Discovering sparse control strategies in C. elegans
摘要：
神经或基因调控网络等生物回路使用内部状态将感官输入映射到适应性行为库。描述这种映射是系统生物学面临的一个主要挑战，尽管探测内部状态的实验正在迅速发展，但鉴于内部状态可以映射到行为的许多可能方式，生物体的复杂性构成了一个根本性障碍。利用C. elegans作为一个例子，我们提出了一个协议的系统扰动的神经状态，限制实验的复杂性，但仍然表征集体方面的神经行为地图。我们认为实验动机的小扰动-最有可能保持自然动态和更接近内部控制机制-神经状态及其对集体神经行为的影响。然后，我们将这种扰动连接到集体统计的局部信息几何，这可以完全使用成对扰动来表征。将该协议应用于C. elegans的神经活动，我们发现集体神经统计是最敏感的几个主要扰动模式。主导特征值最初按幂律衰减，揭示了个体神经活动和成对相互作用的变化所产生的层次结构。最高等级的模式往往是由几个占系统大部分灵敏度的“关键”神经元主导的，这表明控制集体行为的稀疏机制。
英文摘要：
Biological circuits such as neural or gene regulation networks use internal states to map sensory input to an adaptive repertoire of behavior. Characterizing this mapping is a major challenge for systems biology, and though experiments that probe internal states are developing rapidly, organismal complexity presents a fundamental obstacle given the many possible ways internal states could map to behavior. Using C. elegans as an example, we propose a protocol for systematic perturbation of neural states that limits experimental complexity but still characterizes collective aspects of the neural-behavioral map. We consider experimentally motivated small perturbations -- ones that are most likely to preserve natural dynamics and are closer to internal control mechanisms -- to neural states and their impact on collective neural behavior. Then, we connect such perturbations to the local information geometry of collective statistics, which can be fully characterized using pairwise perturbations. Applying the protocol to a minimal model of C. elegans neural activity, we find that collective neural statistics are most sensitive to a few principal perturbative modes. Dominant eigenvalues decay initially as a power law, unveiling a hierarchy that arises from variation in individual neural activity and pairwise interactions. Highest-ranking modes tend to be dominated by a few, "pivotal" neurons that account for most of the system's sensitivity, suggesting a sparse mechanism for control of collective behavior.
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