标题： 探索循环神经网络中权重初始化、解的多样性以及在时间与决策任务训练中的退化现象 显示英文标题

标题： Exploring weight initialization, diversity of solutions, and degradation in recurrent neural networks trained for temporal and decision-making tasks

摘要： 循环神经网络（RNNs）常用于建模大脑功能和结构的方面。 在本工作中，我们训练了小型全连接RNNs，以执行具有时变刺激的时间和流控制任务。 我们的结果表明，不同的RNN可以通过收敛到不同的底层动力学来解决相同的任务，并且还展示了当网络规模减小、间隔持续时间增加或连接损伤增加时，性能如何优雅地退化。 对于所考虑的任务，我们探讨了训练后获得的网络根据任务参数化可以有多稳健。 在此过程中，我们开发了一个框架，可用于对计算神经科学中的其他感兴趣任务进行参数化。 我们的结果有助于量化模型的不同方面，这些模型通常被用作黑箱，需要被理解以模拟大脑皮层区域的生物反应。 显示英文摘要

摘要： Recurrent Neural Networks (RNNs) are frequently used to model aspects of brain function and structure. In this work, we trained small fully-connected RNNs to perform temporal and flow control tasks with time-varying stimuli. Our results show that different RNNs can solve the same task by converging to different underlying dynamics and also how the performance gracefully degrades as either network size is decreased, interval duration is increased, or connectivity damage is increased. For the considered tasks, we explored how robust the network obtained after training can be according to task parameterization. In the process, we developed a framework that can be useful to parameterize other tasks of interest in computational neuroscience. Our results are useful to quantify different aspects of the models, which are normally used as black boxes and need to be understood in order to model the biological response of cerebral cortex areas.