Metazoans gather information from their environments and respond in predictable ways. These computational tasks are achieved with neural networks of varying complexity. Their performance must be reliable over an individual’s lifetime while dealing with the shorter lifespan of cells and connection failure—thus rendering ageing a relevant feature. How do computations degrade over an organism’s lifespan? How reliable can they remain throughout? We tackle these questions with a multi-objective optimization approach. We demand that digital organisms equipped with neural networks solve a computational task reliably over an extended lifespan. Neural connections are costly (as an associated metabolism in living beings). They also degrade over time, but can be regenerated at some expense. We investigate the simultaneous minimization of both these costs and the computational error. Pareto optimal trade-offs emerge with designs displaying a broad range of solutions: from small networks with high regeneration rate, to large, redundant circuits that regenerate slowly. The organism’s lifespan and the external damage act as evolutionary pressures. They improve the exploration of the space of solutions and impose tighter optimality constraints. Large damage rates can also constrain the space of possibilities, forcing the commitment of organisms to unique strategies for neural systems maintenance.

中文翻译：

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衰老、计算和神经再生过程的进化

后生动物从它们的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不同复杂度的神经网络来实现的。它们的性能必须在个人的一生中可靠，同时应对电池寿命较短和连接故障——从而使老化成为一个相关的特征。计算如何在生物体的生命周期中退化？它们在整个过程中的可靠性如何？我们使用多目标优化方法来解决这些问题。我们要求配备神经网络的数字生物在更长的生命周期内可靠地解决计算任务。神经连接是昂贵的（作为生物体内的相关代谢）。它们也会随着时间的推移而降解，但可以以一定的代价再生。我们研究同时最小化这些成本和计算误差。帕累托最优权衡出现在显示广泛解决方案的设计中：从具有高再生率的小型网络到再生缓慢的大型冗余电路。生物体的寿命和外部损伤是进化压力。它们改进了对解决方案空间的探索，并施加了更严格的优化约束。大的损伤率也会限制可能性的空间，迫使生物体采取独特的神经系统维护策略。生物体的寿命和外部损伤是进化压力。它们改进了对解决方案空间的探索，并施加了更严格的优化约束。大的损伤率也会限制可能性的空间，迫使生物体采取独特的神经系统维护策略。生物体的寿命和外部损伤是进化压力。它们改进了对解决方案空间的探索，并施加了更严格的优化约束。大的损伤率也会限制可能性的空间，迫使生物体采取独特的神经系统维护策略。