Humans excel at continually acquiring, consolidating, and retaining
information from an ever-changing environment, whereas artificial neural
networks (ANNs) exhibit catastrophic forgetting. There are considerable
differences in the complexity of synapses, the processing of information, and
the learning mechanisms in biological neural networks and their artificial
counterparts, which may explain the mismatch in performance. We consider a
biologically plausible framework that constitutes separate populations of
exclusively excitatory and inhibitory neurons that adhere to Dale's principle,
and the excitatory pyramidal neurons are augmented with dendritic-like
structures for context-dependent processing of stimuli. We then conduct a
comprehensive study on the role and interactions of different mechanisms
inspired by the brain, including sparse non-overlapping representations,
Hebbian learning, synaptic consolidation, and replay of past activations that
accompanied the learning event. Our study suggests that the employing of
multiple complementary mechanisms in a biologically plausible architecture,
similar to the brain, may be effective in enabling continual learning in ANNs.

通过生物学上的神经元网络组合中的多种互补机制，可以在人工神经网络中实现连续学习。

生物可信神经网络的研究：大脑启发机制在持续学习中的作用和交互

A Study of Biologically Plausible Neural Network: The Role and  Interactions of Brain-Inspired Mechanisms in Continual Learning

Continual learning (CL) in the brain is facilitated by a complex set of
mechanisms. This includes the interplay of multiple memory systems for
consolidating information as posited by the complementary learning systems
(CLS) theory and synaptic consolidation for protecting the acquired knowledge
from erasure. Thus, we propose a general CL method that creates a synergy
between SYNaptic consolidation and dual memory Experience Replay (SYNERgy). Our
method maintains a semantic memory that accumulates and consolidates
information across the tasks and interacts with the episodic memory for
effective replay. It further employs synaptic consolidation by tracking the
importance of parameters during the training trajectory and anchoring them to
the consolidated parameters in the semantic memory. To the best of our
knowledge, our study is the first to employ dual memory experience replay in
conjunction with synaptic consolidation that is suitable for general CL whereby
the network does not utilize task boundaries or task labels during training or
inference. Our evaluation on various challenging CL scenarios and
characteristics analyses demonstrate the efficacy of incorporating both
synaptic consolidation and CLS theory in enabling effective CL in DNNs.

该研究提出了一种基于 SYNaptic consolidation 和 dual memory experience replay 的通用 CL 方法，通过维护语义记忆来实现跨任务信息的累积和巩固，并结合突触巩固以实现高效的连续学习，不需要使用任务标签。

突触巩固和体验回放在通用持续学习中的协同作用

SYNERgy between SYNaptic consolidation and Experience Replay for general continual learning

Artificial neural networks face the well-known problem of catastrophic
forgetting. What's worse, the degradation of previously learned skills becomes
more severe as the task sequence increases, known as the long-term catastrophic
forgetting. It is due to two facts: first, as the model learns more tasks, the
intersection of the low-error parameter subspace satisfying for these tasks
becomes smaller or even does not exist; second, when the model learns a new
task, the cumulative error keeps increasing as the model tries to protect the
parameter configuration of previous tasks from interference. Inspired by the
memory consolidation mechanism in mammalian brains with synaptic plasticity, we
propose a confrontation mechanism in which Adversarial Neural Pruning and
synaptic Consolidation (ANPyC) is used to overcome the long-term catastrophic
forgetting issue. The neural pruning acts as long-term depression to prune
task-irrelevant parameters, while the novel synaptic consolidation acts as
long-term potentiation to strengthen task-relevant parameters. During the
training, this confrontation achieves a balance in that only crucial parameters
remain, and non-significant parameters are freed to learn subsequent tasks.
ANPyC avoids forgetting important information and makes the model efficient to
learn a large number of tasks. Specifically, the neural pruning iteratively
relaxes the current task's parameter conditions to expand the common parameter
subspace of the task; the synaptic consolidation strategy, which consists of a
structure-aware parameter-importance measurement and an element-wise parameter
updating strategy, decreases the cumulative error when learning new tasks. The
full source code is available at this https URL

通过在大脑的突触可塑性中受到启发，作者提出了一种对抗神经修剪和突触巩固（ANPyC）机制来处理由于任务序列增加而导致的长期灾难性遗忘问题，其中神经修剪充当长期沮丧来修剪与任务无关的参数，而新颖的突触巩固则充当长期增强来增强与任务相关的参数。