# Dynamical learning of dynamics

## TL;DR

This paper demonstrates that fixed-weight neural networks can rapidly learn and generate new dynamics through imitation after pretraining, enabling quick adaptation to various complex tasks without ongoing feedback.

## Contribution

It introduces a method where fixed-weight networks learn new dynamics through imitation, showing rapid adaptation after pretraining, which differs from traditional slow synaptic learning.

## Key findings

- Networks can learn diverse dynamics like oscillations and chaos.
- Pretrained fixed-weight networks adapt quickly to new tasks.
- Networks maintain learned dynamics without further feedback.

## 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.