We develop a novel credit assignment algorithm for information processing with spiking neurons without requiring feedback synapses. Specifically, we propose an event-driven generalization of the forward-forward and the predictive forward-forward learning processes for a spiking neural system that iteratively processes sensory input over a stimulus window. As a result, the recurrent circuit computes the membrane potential of each neuron in each layer as a function of local bottom-up, top-down, and lateral signals, facilitating a dynamic, layer-wise parallel form of neural computation. Unlike spiking neural coding, which relies on feedback synapses to adjust neural electrical activity, our model operates purely online and forward in time, offering a promising way to learn distributed representations of sensory data patterns with temporal spike signals. Notably, our experimental results on several pattern datasets demonstrate that the even-driven forward-forward (ED-FF) framework works well for training a dynamic recurrent spiking system capable of both classification and reconstruction.

翻译：我们提出了一种无需反馈突触的脉冲神经元信息处理新型信用分配算法。具体而言，针对在刺激窗口内迭代处理感觉输入的脉冲神经系统，我们构建了前馈-前馈学习过程与预测性前馈-前馈学习过程的事件驱动泛化形式。该递归回路通过计算各层神经元膜电位——该电位是局部自底向上信号、自顶向下信号及侧向信号的函数——实现了动态、层间并行的神经计算模式。与依赖反馈突触调节神经电活动的脉冲神经编码不同，我们的模型仅以在线前向时序方式运行，为利用时序脉冲信号学习感觉数据模式的分布式表征提供了新途径。值得注意的是，在多个模式数据集上的实验结果表明，事件驱动前馈-前馈（ED-FF）框架能有效训练兼具分类与重建能力的动态递归脉冲系统。