Forward propagation of errors through time (nicolaszucchet.github.io)

Researchers have recently introduced a novel method for training recurrent neural networks (RNNs) by proposing the forward propagation of errors through time (FPTT). Unlike the traditional backpropagation through time (BPTT), which calculates error signals in reverse and inherits significant memory and computational demands, FPTT innovatively propagates errors forward. This method utilizes a "warm-up" phase to establish initial conditions for error trajectories required for learning, offering a potential alternative to BPTT that aligns better with the principles of neuromorphic hardware and biological cognition. Despite its promising theoretical foundation, FPTT faces critical numerical stability issues. As neural networks transition into a "forgetting" state, the algorithm tends to become unstable, leading to rapid loss of information necessary for effective learning. While the concept itself showcases a possible paradigm shift in error propagation, the researchers concluded that the numerical challenges currently prevent its widespread application. Nonetheless, this exploration contributes to the ongoing search for more efficient training techniques and may inspire future advancements in AI that seek to overcome similar limits in recurrent network architectures.