I will describe how alternatives to conventional neural networks that are very loosely biologically inspired can improve meta-learning, including continual learning. First I will summarize differentiable Hebbian learning and differentiable neuromodulated Hebbian learning (aka “backpropamine”). Both are techniques for training deep neural networks with synaptic plasticity, meaning the weights can change during meta-testing/inference. Whereas meta-learned RNNs can only store within-episode information in their activations, such plastic Hebbian networks can store information in their weights in addition to its activations, improving performance on some classes of problems. Second, I will describe a new, unpublished method that improves the state of the art in continual learning. ANML (A Neuromodulated Meta-Learning algorithm) meta-learns a neuromodulatory network that gates the activity of the main prediction network, enabling the learning of up to 600 simple tasks sequentially.