Backpropagation is widely used to train artificial neural networks, but its relationship to
synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely …

Abstract Goal-conditioned Reinforcement Learning (RL) aims at learning optimal policies,
given goals encoded in special command inputs. Here we study goal-conditioned neural …

Applying feature dependent network weights have been proved to be effective in many
fields. However, in practice, restricted by the enormous size of model parameters and …

We study the problem of learning associative memory--a system which is able to retrieve a
remembered pattern based on its distorted or incomplete version. Attractor networks provide …

The search for biologically faithful synaptic plasticity rules has resulted in a large body of
models. They are usually inspired by--and fitted to--experimental data, but they rarely …

Abstract Warped Gradient descent (WarpGrad) is a remarkable meta-learning method for
gradient transformation by inserting warp-layers. However, the task-shared initialization …

Meta Learning automates the search for learning algorithms. At the same time, it creates a
dependency on human engineering on the meta-level, where meta learning algorithms …

Short-term plasticity (STP) is a mechanism that stores decaying memories in synapses of the
cerebral cortex. In computing practice, STP has been used, but mostly in the niche of spiking …

Abstract Knowledge tracing (KT) refers to the issue of predicting learners' knowledge states
based on their learning history and is the core technology for computer-assisted adaptive …

Designing evolutionary algorithms capable of uncovering highly evolvable representations
is an open challenge in evolutionary computation; such evolvability is important in practice …