Topic: biologically plausible learning rules

Learning in cortical multi-layer networks is thought to occur through spatially and temporally local synaptic plasticity aided by ubiquitous feedback connections from upper layers. However, the known biologically plausible learning rules for such networks are yet to reach the high performance of the machine learning algorithms, such as backpropagation [1], on difficult tasks that the brain is capable of solving. It has been shown that one of the obstacles to backpropagation-like algorithm implementation in the brain, the ’weight transport’ problem, can be overcome with initially random feedbac

Neuroscientists are increasingly turning to the mathematical framework of artificial neural networks (ANNs) training for insights into biological learning mechanisms. This has motivated an influx of biologically plausible learning rules that approximate backpropagation [1-9]. Despite achieving impressive performance quantified by accuracy, these studies have not covered the breadth of solution characteristics found by these rules. In this work, we leverage established theoretical tools from deep learning to investigate the robustness of solutions, and gain insights into generalization properti