Recurrent / LSTM layers explained in a simple way

For all the previously introduced layers, the same output will be generated if we repeat the same input several times. For instance, if we have a linear layer with f(x) 2.x. Each time we ask to predict f(3) we will get 6. Now imagine we are training an algorithm to detect repetitions, so we want that F(3) 0 for the first time (no repetition detected), then we would like to get F(3) 1 for the second time. We can't achieve this behavior with non-recurrent layers.