Damage to the hippocampal system disrupts recent memory but leaves remote memory
intact. The account presented here suggests that memories are first stored via synaptic …

All natural cognitive systems, and, in particular, our own, gradually forget previously learned
information. Plausible models of human cognition should therefore exhibit similar patterns of …

Artificial neural networks thrive in solving the classification problem for a particular rigid task,
acquiring knowledge through generalized learning behaviour from a distinct training phase …

Continual learning, the setting where a learning agent is faced with a never-ending stream
of data, continues to be a great challenge for modern machine learning systems. In …

Artificial neural networks thrive in solving the classification problem for a particular rigid task,
where the network resembles a static entity of knowledge, acquired through generalized …

Current deep learning methods are regarded as favorable if they empirically perform well on
dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual …

Incremental learning refers to learning from streaming data, which arrive over time, with
limited memory resources and, ideally, without sacrificing model accuracy. This setting fits …

The stability-plasticity dilemma is a wellknow constraint for artificial and biological neural
systems. The basic idea is that learning in a parallel and distributed system requires …

This paper reviews the problem of catastrophic forgetting (the loss or disruption of previously
learned information when new information is learned) in neural networks, and explores …

This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving
neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their …