  Artificial neural networks (ANNs) are typically confined to accomplishing
pre-defined tasks by learning a set of static parameters. In contrast,
biological neural networks (BNNs) can adapt to various new tasks by continually
updating the neural connections based on the inputs, which is aligned with the
paradigm of learning effective learning rules in addition to static parameters,
e.g., meta-learning. Among various biologically inspired learning rules,
Hebbian plasticity updates the neural network weights using local signals
without the guide of an explicit target function, thus enabling an agent to
learn automatically without human efforts. However, typical plastic ANNs using
a large amount of meta-parameters violate the nature of the genomics bottleneck
and potentially deteriorate the generalization capacity. This work proposes a
new learning paradigm decomposing those connection-dependent plasticity rules
into neuron-dependent rules thus accommodating $\Theta(n^2)$ learnable
parameters with only $\Theta(n)$ meta-parameters. We also thoroughly study the
effect of different neural modulation on plasticity. Our algorithms are tested
in challenging random 2D maze environments, where the agents have to use their
past experiences to shape the neural connections and improve their performances
for the future. The results of our experiment validate the following: 1.
Plasticity can be adopted to continually update a randomly initialized RNN to
surpass pre-trained, more sophisticated recurrent models, especially when
coming to long-term memorization. 2. Following the genomics bottleneck, the
proposed decomposed plasticity can be comparable to or even more effective than
canonical plasticity rules in some instances.
