  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.
