衰老、计算和神经再生过程的进化
Aging, computation, and the evolution of neural regeneration processes
摘要：
后生动物能够从环境中收集信息，并以可预测的方式做出反应。这些计算任务是通过或多或少复杂的神经元网络来实现的。任务性能必须在个人的一生中保持可靠，并且必须稳健地处理细胞的有限寿命或连接故障-使衰老成为这种情况下的相关特征。计算是如何在有机体的生命周期中退化的？整个过程中计算的可靠性如何？为了回答这些问题，在这里，我们的方法下的多目标（帕累托）优化方法的问题。我们考虑一个数字生物的人口配备了一个神经网络，必须解决一个给定的计算任务可靠。我们要求它们在更长的使用寿命内保持功能（尽可能可靠）。神经连接是昂贵的（作为生物的相关代谢），并随着时间的推移而退化。它们也可以以一定的代价再生。我们调查的同时最小化的代谢负担（由于连接和再生成本）和计算误差和权衡出现。我们表明，帕累托最优设计显示了广泛的潜在的解决方案：从小型网络与高再生率，以较大的和冗余的电路，再生缓慢。生物体的寿命和外部损伤率被发现作为一个进化的压力，提高了探索的空间的解决方案，并提出更严格的最优性条件。我们还发现，电路的大损伤率可以限制可能的空间，并使生物体致力于维护神经系统的独特策略。
英文摘要：
Metazoans are capable of gathering information from their environments and respond in predictable ways. These computational tasks are achieved by means of more or less complex networks of neurons. Task performance must be reliable over an individual's lifetime and must deal robustly with the finite lifespan of cells or with connection failure - rendering aging a relevant feature in this context. How do computations degrade over an organism's lifespan? How reliable can computations remain throughout? In order to answer these questions, here we approach the problem under a multiobjective (Pareto) optimization approach. We consider a population of digital organisms equipped with a neural network that must solve a given computational task reliably. We demand that they remain functional (as reliable as possible) for an extended lifespan. Neural connections are costly (as an associated metabolism in living beings) and degrade over time. They can also be regenerated at some expense. We investigate the simultaneous minimization of the metabolic burden (due to connections and regeneration costs) and the computational error and the tradeoffs emerging thereof. We show that Pareto optimal designs display a broad range of potential solutions: from small networks with high regeneration rate, to larger and redundant circuits that regenerate slowly. The organism's lifespan and the external damage rates are found to act as an evolutionary pressure that improve the exploration of the space of solutions and poses tighter optimality conditions. We also find that large damage rates to the circuits can constrain the space of the possible and pose organisms to commit to unique strategies for neural systems maintenance.
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。
