We propose the predictive forward-forward (PFF) algorithm for conducting credit assignment in neural systems. Specifically, we design a novel, dynamic recurrent neural system that learns a directed generative circuit jointly and simultaneously with a representation circuit, integrating learnable lateral competition and elements of predictive coding, an emerging and viable neurobiological process theory of cortical function, with the forward-forward (FF) adaptation scheme. Furthermore, PFF efficiently learns to propagate learning signals and updates synapses with forward passes only, eliminating key structural and computational constraints imposed by a backpropagation-based scheme. Besides computational advantages, the PFF process could prove useful for understanding the learning mechanisms behind biological neurons that use local signals despite missing feedback connections. We run experiments on image data and demonstrate that the PFF procedure works as well as backpropagation of errors, offering a promising brain-inspired learning algorithm for classifying, reconstructing, and synthesizing data patterns.

翻译：我们提出预测性前向-前向（PFF）算法，用于在神经系统中进行信用分配。具体而言，我们设计了一种新颖的动态递归神经系统，它同时联合学习一个有向生成电路和一个表征电路，将可学习的侧向竞争和预测编码要素——一种新兴且具有生物学可行性的皮层功能神经生物学过程理论——与前向-前向（FF）适应方案相整合。此外，PFF仅通过前向传播高效地学习传播学习信号并更新突触，消除了基于反向传播方案所施加的关键结构与计算约束。除了计算优势外，PFF过程可能有助于理解利用局部信号但缺乏反馈连接的生物神经元的学习机制。我们在图像数据上进行实验，证明PFF过程能达到与误差反向传播相当的效果，为分类、重构和合成数据模式提供了一种有前景的脑启发学习算法。