Linguistics

Cognitive psychology

Engineering

Philosophy

Perceptron

Systems engineering

Operating system

Computer science

Forgetting

Psychology

MNIST database

Task (project management)

Artificial intelligence

Redundancy (engineering)

Artificial neural network

Unsupervised learning

Convolutional neural network

Machine learning

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits

Continual learning is the ability to acquire a new task or knowledge without losing any previously collected information. Achieving continual learning in artificial intelligence (AI) is currently prevented by catastrophic forgetting, where training of a new task deletes all previously learned tasks. Here, we present a new concept of a neural network capable of combining supervised convolutional learning with bio-inspired unsupervised learning. Brain-inspired concepts such as spike-timing-dependent plasticity (STDP) and neural redundancy are shown to enable continual learning and prevent catastrophic forgetting without compromising standard accuracy achievable with state-of-the-art neural networks. Unsupervised learning by STDP is demonstrated by hardware experiments with a one-layer perceptron adopting phase-change memory (PCM) synapses. Finally, we demonstrate full testing classification of Modified National Institute of Standards and Technology (MNIST) database with an accuracy of 98% and continual learning of up to 30% non-trained classes with 83% average accuracy.

Unsupervised Learning to Overcome Catastrophic Forgetting in Neural Networks