There are a lot of neural networks, each with their own represential capabilities. + Reservoir computing, ESN, LSM, only combines quenched dynamics. + Adaptiv

There are a lot of neural networks, each with their own represential capabilities. + Reservoir computing, ESN, LSM, only combines quenched dynamics. + Adaptive resonance theory. Addresses catastrophic forgetting and allows someone to learn from one example. + Bottleneck networks. Forcing networks to represent things in a compressed sense. Almost like making up their own symbols. + Global workspace theory. Winner take all mechanisms that allow modules to compete. + Polychronization. Izhikevich shows how dynamic representations are possible thanks to delays. + Attractor networks. Use of dynamical system theory to have population of neurons perform computational tasks. That neural networks are too fragile is a statement that's a bit too general.