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. Notably, the system integrates learnable lateral competition, noise injection, 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 backpropagation-based schemes. 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, offering a promising brain-inspired 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，该系统的目标是在学习表示电路的同时，同步学习有向生成电路。值得注意的是，该系统将可学习的侧向竞争、噪声注入以及预测编码（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）的元素，与前向-前向（FF）适应方案相结合。此外，PFF算法能够高效地仅通过前向传递来学习传播学习信号并更新突触，从而消除了基于反向传播的方案所施加的关键结构和计算约束。除计算优势外，PFF过程可能有助于理解那些虽缺乏反馈连接但仍利用局部信号的生物神经元的学习机制。我们在图像数据上进行了实验，证明PFF过程的表现与反向传播相当，为分类、重建和合成数据模式提供了一种有前景的类脑算法。