Bray Samuel R, Wyss Livia S, Chai Chew, Lozada Maria E, Wang Bo
Department of Bioengineering, Stanford University, Stanford, CA, USA.
Department of Biology, Stanford University, Stanford, CA, USA.
bioRxiv. 2023 Jan 23:2023.01.20.523817. doi: 10.1101/2023.01.20.523817.
Animal behavior emerges from collective dynamics of interconnected neurons, making it vulnerable to connectome damage. Paradoxically, many organisms maintain significant behavioral output after large-scale neural injury. Molecular underpinnings of this extreme robustness remain largely unknown. Here, we develop a quantitative behavioral analysis pipeline to measure previously uncharacterized long-lasting latent memory states in planarian flatworms during whole-brain regeneration. By combining >20,000 animal trials with neural population dynamic modeling, we show that long-range volumetric peptidergic signals allow the planarian to rapidly reestablish latent states and restore coarse behavior after large structural perturbations to the nervous system, while small-molecule neuromodulators gradually refine the precision. The different time and length scales of neuropeptide and small-molecule transmission generate incoherent patterns of neural activity which competitively regulate behavior and memory. Controlling behavior through opposing communication mechanisms creates a more robust system than either alone and may serve as a generic approach to construct robust neural networks.
动物行为源自相互连接的神经元的集体动力学，这使其容易受到连接组损伤的影响。矛盾的是，许多生物体在大规模神经损伤后仍能保持显著的行为输出。这种极端鲁棒性的分子基础在很大程度上仍然未知。在这里，我们开发了一种定量行为分析流程，以测量涡虫在全脑再生过程中以前未被表征的长期潜在记忆状态。通过将超过20000次动物试验与神经群体动态建模相结合，我们表明，长距离体积肽能信号使涡虫在神经系统受到大的结构扰动后能够迅速重新建立潜在状态并恢复粗略行为，而小分子神经调节剂则逐渐提高行为的精确性。神经肽和小分子传递的不同时间和长度尺度产生了不连贯的神经活动模式，这些模式竞争性地调节行为和记忆。通过相反的通信机制控制行为创建了一个比单独任何一种机制都更强大的系统，并且可能是构建强大神经网络的一种通用方法。
