Abstract
According to Hebbian theory, synaptic plasticity is the ability of
neurons to strengthen or weaken the synapses among them in response to
stimuli. It plays a fundamental role in the processes of learning and
memory of biological neural networks. With plasticity, biological agents
can adapt on multiple timescales and outclass artificial agents, the
majority of which still rely on static Artificial Neural Network (ANN)
controllers. In this work, we focus on Voxel-based Soft Robots (VSRs), a
class of simulated artificial agents, composed as aggregations of
elastic cubic blocks. We propose a Hebbian ANN controller where every
synapse is associated with a Hebbian rule that controls the way the
weight is adapted during the VSR lifetime. For a given task and
morphology, we optimize the controller for the task of locomotion by
evolving, rather than the weights, the parameters of the Hebbian rules.
Our results show that the Hebbian controller is comparable, often better
than a non-Hebbian baseline and that it is more adaptable to unforeseen
damages. We also provide novel insights into the inner workings of
plasticity and demonstrate that “true” learning does take place, as
the evolved controllers improve over the lifetime and generalize well.