Biologically plausible gated recurrent neural networks for working memory and learning-to-learn

The acquisition of knowledge and skills does not occur in isolation but learning experiences amalgamate within and across domains. The process through which learning can accelerate over time is referred to as learning-to-learn or meta-learning. While meta-learning can be implemented in recurrent neural networks, these networks tend to be trained with architectures that are not easily interpretable or mappable to the brain and with learning rules that are biologically implausible. Specifically, these rules have often employed backpropagation-through-time, which relies on information that is unavailable at synapses that are undergoing plasticity in the brain. Previous studies that exclusively used local information for their weight updates had a limited capacity to integrate information over long timespans and could not easily learn-to-learn. Here, we propose a novel gated memory network named RECOLLECT, which can flexibly retain or forget information by means of a single memory gate and is trained with a biologically plausible trial-and-error-learning that requires only local information. We demonstrate that RECOLLECT successfully learns to represent task-relevant information over increasingly long memory delays in a pro-/anti-saccade task, and that it learns to flush its memory at the end of a trial. Moreover, we show that RECOLLECT can learn-to-learn an effective policy on a reversal bandit task. Finally, we show that the solutions acquired by RECOLLECT resemble how animals learn similar tasks.