Before revision:  Using these insights, we suggest steps towards a more biologically plausible version of FORCE, as well as an approach to design optimal training protocols .
After revision:  Using these insights, we suggest steps towards a more biologically plausible version of the FORCE algorithm, is constantly obstructed by URLetting, which is manifested as the destruction of dynamical objects from previous trials. The degree of interference is determined by the correlation between different trials. We show which specific ingredients of FORCE avoid this phenomenon. Overall, this difference results in convergence that is orders of magnitude slower for LMS. Learning implies accumulating information across multiple trials to form the overall concept of the task. Our results show that interference between trials can greatly affect learning, in a learning rule dependent manner. These insights can help design experimental protocols that minimize such interference, and possibly infer underlying learning rules by observing behavior and neural activity throughout learning .