The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output. However, the majority of current attempts at biologically-plausible learning methods are either non-local in time, require highly specific connectivity motives, or have no clear link to any known mathematical optimization method. Here, we introduce Deep Feedback Control (DFC), a new learning method that uses a feedback controller to drive a deep neural network to match a desired output target and whose control signal can be used for credit assignment. The resulting learning rule is fully local in space and time and approximates Gauss-Newton optimization for a wide range of feedback connectivity patterns. To further underline its biological plausibility, we relate DFC to a multi-compartment model of cortical pyramidal neurons with a local voltage-dependent synaptic plasticity rule, consistent with recent theories of dendritic processing. By combining dynamical system theory with mathematical optimization theory, we provide a strong theoretical foundation for DFC that we corroborate with detailed results on toy experiments and standard computer-vision benchmarks.
翻译:深层学习的成功激发了人们对于大脑是否通过使用类似技术对每个神经重量的功率分配对网络输出的贡献的信用感知的兴趣。然而,目前大多数生物可观学习方法的尝试要么是非局部的,有时需要非常具体的连通动机,或者与任何已知的数学优化方法没有明确的联系。这里,我们引入了深反馈控制(DFC)这一新学习方法,利用反馈控制器驱动一个深神经网络,以匹配期望的产出目标,并且其控制信号可用于信用分配。由此形成的学习规则在空间和时间上是完全本地的,在广泛反馈连接模式方面,高斯-牛顿的优化是近似本地的。为了进一步强调其生物可信任性,我们把DFC与多兼容的金字塔神经模型与一个当地依赖电压的合成塑料规则联系起来,这与最近的斜度处理理论是一致的。通过将动态系统理论与数学优化理论相结合,我们为DFCC提供了坚实的理论基础,我们用详细的微量实验和标准计算机基准加以证实。