Continuous Learning in a Single-Incremental-Task Scenario with Spike Features
This addresses the problem of catastrophic forgetting for AI systems requiring continuous learning, though it is incremental as it builds on existing bio-inspired and regularization methods.
The paper tackled catastrophic forgetting in deep neural networks by combining bio-inspired spike timing dependent plasticity for feature extraction with a modified synaptic intelligence regularizer, achieving improved sequential learning on MNIST digits split into five sub-tasks.
Deep Neural Networks (DNNs) have two key deficiencies, their dependence on high precision computing and their inability to perform sequential learning, that is, when a DNN is trained on a first task and the same DNN is trained on the next task it forgets the first task. This phenomenon of forgetting previous tasks is also referred to as catastrophic forgetting. On the other hand a mammalian brain outperforms DNNs in terms of energy efficiency and the ability to learn sequentially without catastrophically forgetting. Here, we use bio-inspired Spike Timing Dependent Plasticity (STDP)in the feature extraction layers of the network with instantaneous neurons to extract meaningful features. In the classification sections of the network we use a modified synaptic intelligence that we refer to as cost per synapse metric as a regularizer to immunize the network against catastrophic forgetting in a Single-Incremental-Task scenario (SIT). In this study, we use MNIST handwritten digits dataset that was divided into five sub-tasks.