full-FORCE：一种基于目标的训练循环网络方法
full-FORCE: A Target-Based Method for Training Recurrent Networks
AI总结：
本文提出一种基于目标的循环网络训练方法，通过引入第二网络提供目标动态，实现更高效的任务处理，具有更少的神经元和更高的噪声鲁棒性。
摘要：
训练好的循环网络是建模动态神经计算的强大工具。我们提出了一种基于目标的方法，用于修改循环网络的全连接矩阵，以训练其执行涉及时间复杂输入/输出转换的任务。该方法在训练过程中引入第二个网络，提供合适的“目标”动态，有助于完成任务。由于利用了全循环连接，该方法产生的网络在执行任务时比传统的最小二乘（FORCE）方法使用更少的神经元，并具有更高的噪声鲁棒性。此外，我们展示了如何通过向目标生成网络引入额外的输入信号，这些信号作为任务提示，大大扩展了可学习的任务范围，并提供了对训练任务执行网络动态复杂性和性质的控制。
英文摘要：
Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations. The method introduces a second network during training to provide suitable "target" dynamics useful for performing the task. Because it exploits the full recurrent connectivity, the method produces networks that perform tasks with fewer neurons and greater noise robustness than traditional least-squares (FORCE) approaches. In addition, we show how introducing additional input signals into the target-generating network, which act as task hints, greatly extends the range of tasks that can be learned and provides control over the complexity and nature of the dynamics of the trained, task-performing network.
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。
