That might be true for echo state networks, but reservoir computing as a whole has other, more powerful models, like Liquid State Machines (which use a randomly connected spiking neural network in the reservoir, which isn’t trivially trainable). If you could get one of those working in Flux or Knet (which would definitely be a bit more difficult than an ESN), it would be considerably more powerful in my opinion.
EDIT: To respond to the initial question that was asked: Flux can definitely make this work, you’d just have to make sure to only consider the connections from reservoir to readout layer in your training function. If you go with an LSM, you’ll also need to decide how you want to convert from spikes to continuous values, which will need to be differentiable.
I’ve never used Knet before, so I won’t say whether or not it’s possible (although it probably is).