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Trained recurrent networks are powerful tools for modeling dynamic neuralcomputations. We present a target-based method for modifying the fullconnectivity matrix of a recurrent network to train it to perform tasksinvolving temporally complex input/output transformations. The methodintroduces a second network during training to provide suitable "target"dynamics useful for performing the task. Because it exploits the full recurrentconnectivity, the method produces networks that perform tasks with fewerneurons and greater noise robustness than traditional least-squares (FORCE)approaches. In addition, we show how introducing additional input signals intothe target-generating network, which act as task hints, greatly extends therange of tasks that can be learned and provides control over the complexity andnature of the dynamics of the trained, task-performing network.

full-FORCE: A Target-Based Method for Training Recurrent Networks