In recent years, there has been an intense debate about how learning in biological neural networks (BNNs) differs from learning in artificial neural networks. It is often argued that the updating of connections in the brain relies only on local information, and therefore a stochastic gradient-descent type optimization method cannot be used. In this paper, we study a stochastic model for supervised learning in BNNs. We show that a (continuous) gradient step occurs approximately when each learning opportunity is processed by many local updates. This result suggests that stochastic gradient descent may indeed play a role in optimizing BNNs.

翻译：近年来，关于生物神经网络（BNNs）学习与人工神经网络学习差异的讨论日趋激烈。通常观点认为，大脑中连接强度的更新仅依赖于局部信息，因此无法采用随机梯度下降类型的优化方法。本文针对BNNs中的监督学习构建随机模型，研究表明：当每个学习机会经过多次局部更新处理时，近似呈现出（连续）梯度下降步骤。该结果表明随机梯度下降可能在BNNs优化过程中确实发挥作用。