Deep learning has redefined the field of artificial intelligence (AI) thanks to the rise of artificial neural networks, which are architectures inspired by their neurological counterpart in the brain. Through the years, this dualism between AI and neuroscience has brought immense benefits to both fields, allowing neural networks to be used in dozens of applications. These networks use an efficient implementation of reverse differentiation, called backpropagation (BP). This algorithm, however, is often criticized for its biological implausibility (e.g., lack of local update rules for the parameters). Therefore, biologically plausible learning methods that rely on predictive coding (PC), a framework for describing information processing in the brain, are increasingly studied. Recent works prove that these methods can approximate BP up to a certain margin on multilayer perceptrons (MLPs), and asymptotically on any other complex model, and that zero-divergence inference learning (Z-IL), a variant of PC, is able to exactly implement BP on MLPs. However, the recent literature shows also that there is no biologically plausible method yet that can exactly replicate the weight update of BP on complex models. To fill this gap, in this paper, we generalize (PC and) Z-IL by directly defining them on computational graphs, and show that it can perform exact reverse differentiation. What results is the first biologically plausible algorithm that is equivalent to BP in the way of updating parameters on any neural network, providing a bridge between the interdisciplinary research of neuroscience and deep learning.
Differentiate the Solver, Not the Equation: Reverse-Sweep Adjoints for Block Implicit Simulation
Stanford University（斯坦福大学）；University of California, Los Angeles（加州大学洛杉矶分校）；LightSpeed Studios, Tencent America（腾讯美国光速工作室）；The University of Utah（犹他大学）
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Peking University（北京大学）；Peng Cheng Laboratory（鹏城实验室）
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State
Peking University（北京大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Center for Data Science, Academy for Advanced Interdisciplinary Studies, Peking University（北京大学前沿交叉学科研究院数据科学研究中心）；Pazhou Lab（琶洲实验室）
No Saved Kaleidosope: an 100% Jitted Neural Network Coding Language with Pythonic Syntax
Federal University of Technology - Paraná（巴拉那联邦技术大学）；Federal University of Goias（戈亚斯联邦大学）；São Paulo State University（圣保罗州立大学）；Federal University of Mato Grosso（马托格罗索联邦大学）
