Before revision:  We studied the properties of RNN trained to perform temporal and flow control tasks with temporal stimuli. We present the results regarding three aspects: inner configuration sets, memory capacity with the scale and immunity to induced damage on trained networks .
After revision:  We studied the properties of RNN trained 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 that the performance gracefully degrades either as network size is decreased, interval duration is increased, or connectivity is damaged .