探索用于时间与决策任务的循环神经网络中的权重初始化、解的多样性与退化
Exploring weight initialization, diversity of solutions, and degradation in recurrent neural networks trained for temporal and decision-making tasks
AI总结：
本研究训练小型全连接RNN执行时变刺激下的时间与流量控制任务，发现不同网络可通过不同底层动力学解决同一任务，并揭示了性能随网络规模、时长及损伤的优雅退化规律，同时开发了任务参数化框架以量化模型并辅助建模大脑皮层生物反应。
摘要：
循环神经网络（RNNs）常被用于建模大脑功能与结构的各个方面。在本研究中，我们训练了小型全连接RNNs，以执行具有时变刺激的时间与流量控制任务。结果表明，不同的RNNs能通过收敛到不同的底层动力学来解决同一任务，并展示了当网络规模减小、间隔时长增加或连接损伤增加时，性能是如何优雅退化的。针对所考虑的任务，我们探索了训练后获得的网络在任务参数化下的鲁棒性。在此过程中，我们开发了一个框架，可用于参数化计算神经科学中其他感兴趣的任务。我们的结果有助于量化模型的不同方面，这些模型通常被当作黑盒，需要被理解以便建模大脑皮层区域的生物反应。
英文摘要：
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.
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。
