ARTIFICIAL NEURONAL ENSEMBLES WITH LEARNED CONTEXT DEPENDENT GATING

Abstract

Biological neural networks are capable of recruiting different sets of neurons to encode different memories. However, when training artificial neural networks on a set of tasks, typically, no mechanism is employed for selectively producing anything analogous to these neuronal ensembles. Further, artificial neural networks suffer from catastrophic forgetting, where the network's performance rapidly deteriorates as tasks are learned sequentially. By contrast, sequential learning is possible for a range of biological organisms. We introduce Learned Context Dependent Gating (LXDG), a method to flexibly allocate and recall 'artificial neuronal ensembles', using a particular network structure and a new set of regularization terms. Activities in the hidden layers of the network are modulated by gates, which are dynamically produced during training. The gates are outputs of networks themselves, trained with a sigmoid output activation. The regularization terms we have introduced correspond to properties exhibited by biological neuronal ensembles. The first term penalizes low gate sparsity, ensuring that only a specified fraction of the network is used. The second term ensures that previously learned gates are recalled when the network is presented with input from previously learned tasks. Finally, there is a regularization term responsible for ensuring that new tasks are encoded in gates that are as orthogonal as possible from previously used ones. We demonstrate the ability of this method to alleviate catastrophic forgetting on continual learning benchmarks. When the new regularization terms are included in the model along with Elastic Weight Consolidation (EWC) it achieves better performance on the benchmark 'permuted MNIST' than with EWC alone. The benchmark 'rotated MNIST' demonstrates how similar tasks recruit similar neurons to the artificial neuronal ensemble.

1. INTRODUCTION

1.1 CATASTROPHIC FORGETTING Learning sequentially without forgetting prior tasks is commonly known as continual learning or life-long learning. When an artificial neural network is trained on a task and the same model is then trained on a new task, the model's performance on the initial task tends to drop significantly. The model tends to overwrite parameters that are important to prior learned tasks, leading to the well known problem referred to as 'catastrophic forgetting'. This phenomenon, initially coined as 'catastrophic interference', was first observed in 1989 (McCloskey & Cohen, 1989) and is a longstanding problem encountered when training artificial neural networks. This problem is related to the stability-plasticity dilemma (Mermillod et al., 2013) . The dilemma arises due to the trade off between plasticity when changing parameters for new tasks and stability when keeping them the same for prior learned tasks. The brain is not susceptible to such problems and it is able to adeptly learn many tasks. For example, whilst humans are still quite capable of forgetting, they are able to learn the rules of chess, football, cricket, poker, and many other games sequentially, without forgetting the rules to the others. The mechanisms of how the brain does this are not fully known, though neuroscience experiments examining neuronal activity and plasticity in biological neural networks are providing insights.

