Dynamical learning of dynamics
release_mqfkp3g5vnfiphpqt4xnvgctra
by
Christian Klos, Yaroslav Felipe Kalle Kossio, Sven Goedeke, Aditya Gilra, Raoul-Martin Memmesheimer
2020
Abstract
The ability of humans and animals to quickly adapt to novel tasks is
difficult to reconcile with the standard paradigm of learning by slow synaptic
weight modification. Here we show that fixed-weight neural networks can learn
to generate required dynamics by imitation. After appropriate weight
pretraining, the networks quickly and dynamically adapt to learn new tasks and
thereafter continue to achieve them without further teacher feedback. We
explain this ability and illustrate it with a variety of target dynamics,
ranging from oscillatory trajectories to driven and chaotic dynamical systems.
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1902.02875v3
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