Bray Samuel R, Wyss Livia S, Chai Chew, Lozada Maria E, Wang Bo
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Department of Biology, Stanford University, Stanford, CA 94305, USA.
Cell Rep. 2024 Aug 27;43(8):114580. doi: 10.1016/j.celrep.2024.114580. Epub 2024 Aug 11.
Animal behavior emerges from collective dynamics of neurons, making it vulnerable to damage. Paradoxically, many organisms exhibit a remarkable ability to maintain significant behavior even after large-scale neural injury. Molecular underpinnings of this extreme robustness remain largely unknown. Here, we develop a quantitative pipeline to measure long-lasting latent states in planarian flatworm behaviors during whole-brain regeneration. By combining >20,000 animal trials with neural network modeling, we show that long-range volumetric peptidergic signals allow the planarian to rapidly restore coarse behavior output after large perturbations to the nervous system, while slow restoration of small-molecule neuromodulator functions refines precision. This relies on the different time and length scales of neuropeptide and small-molecule transmission to generate incoherent patterns of neural activity that competitively regulate behavior. Controlling behavior through opposing communication mechanisms creates a more robust system than either alone and may serve as a generalizable approach for constructing robust neural networks.
动物行为源自神经元的集体动力学，因此易受损伤影响。矛盾的是，许多生物体即使在遭受大规模神经损伤后仍表现出维持显著行为的非凡能力。这种极端鲁棒性的分子基础在很大程度上仍不为人知。在此，我们开发了一种定量方法，用于测量涡虫全脑再生过程中行为的长期潜在状态。通过将20000多次动物试验与神经网络建模相结合，我们发现，长程体积肽能信号使涡虫在神经系统受到大的扰动后能迅速恢复粗略的行为输出，而小分子神经调质功能的缓慢恢复则提高了行为的精确性。这依赖于神经肽和小分子传递的不同时间和长度尺度，以产生非相干的神经活动模式，从而竞争性地调节行为。通过相反的通信机制控制行为，会创建一个比单独使用任何一种机制都更强大的系统，这可能是构建强大神经网络的一种通用方法。
