In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Backpropagation is driving today's artificial neural networks. However, despite extensive research, it remains unclear if the brain implements this algorithm. Among neuroscientists, reinforcement learning (RL) algorithms are often seen as a realistic alternative. However, the convergence rate of such learning scales poorly with the number of involved neurons. Here we propose a hybrid learning approach, in which each neuron uses an RL-type strategy to learn how to approximate the gradients that backpropagation would provide. We show that our approach learns to approximate the gradient, and can match the performance of gradient-based learning on fully connected and convolutional networks. Learning feedback weights provides a biologically plausible mechanism of achieving good performance, without the need for precise, pre-specified learning rules.
Perturbations can be used to learn feedback weights on large fully connected and convolutional networks.