Topic: Catastrophic forgetting

Metaplasticity is the change in plasticity of a synapse, and an important mechanism for resolving the stability-plasticity dilemma. However, the exact synaptic or neuron properties that modulate metaplasticity still remains unclear. In this work we use meta-continual learning to discover the importance of factors that contribute to metaplasticity. We use a linear model to assign the relative contribution of four commonly used synaptic properties to metaplasticity: the Hessian, gradient, magnitude of the weights, and activity of the post-synaptic neuron. The coefficients of the linear model ar

The brain is remarkably adept at learning from a continuous stream of data without significantly forgetting previously learnt skills. Conventional machine learning models struggle at continual learning, as weight updates that optimize the current task interfere with previously learnt tasks. A simple remedy to catastrophic forgetting is freezing a network pretrained on a set of base tasks, and training task-specific readouts on this shared trunk. However, this assumes that representations in the frozen network are separable under new tasks, therefore leading to sub-par performance. To continual

Learning is fundamental for interacting with a changing environment. Critical to this process is the ability to rapidly adapt previously acquired skills to external perturbations while avoiding catastrophic forgetting of previous tasks. For the learning system, this corresponds to a trade-off between being able to maintain stable memories of learned tasks, and the ability to swiftly adapt such memories to changes. We hypothesize that such a problem can be addressed by using a two-timescale [1,2] strategy, where memorization and adaptation are performed by two separate modules. This structure a

Continual learning involves a network adapting to new data while retaining previously learned information. In Artificial Neural Networks , this process faces the challenge of catastrophic forgetting, where updating parameters with new data causes an exponential decline in accuracy over time for previously learned tasks. Several methods have been proposed to address this problem and enable continual learning on future neuromorphic hardware. One such neuroscience-inspired method is metaplasticity, which refers to a network's ability to change the learning capacity of its synapses based on its cu

Continual learning focuses on systems that incrementally acquire and update their knowledge. It is a pressing problem in machine learning as it is challenging to learn new tasks without forgetting knowledge about old ones, a phenomenon known as catastrophic forgetting [1]. Since biological systems are able to efficiently learn and update their knowledge continually during their lifetime, we are interested in principles inspired by these systems to try to tackle this challenge. One of the ideas in biology that is often overlooked in artificial neural networks is Dale’s principle [2], stating th