Forward Signal Propagation Learning
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We propose a new learning algorithm for propagating a learning signal and updating neural network parameters via a forward pass, as an alternative to backpropagation. In forward signal propagation learning (sigprop), there is only the forward path for learning and inference, so there are no additional structural or computational constraints on learning, such as feedback connectivity, weight transport, or a backward pass, which exist under backpropagation. Sigprop enables global supervised learning with only a forward path. This is ideal for parallel training of layers or modules. In biology, this explains how neurons without feedback connections can still receive a global learning signal. In hardware, this provides an approach for global supervised learning without backward connectivity. Sigprop by design has better compatibility with models of learning in the brain and in hardware than backpropagation and alternative approaches to relaxing learning constraints. We also demonstrate that sigprop is more efficient in time and memory than they are. To further explain the behavior of sigprop, we provide evidence that sigprop provides useful learning signals in context to backpropagation. To further support relevance to biological and hardware learning, we use sigprop to train continuous time neural networks with Hebbian updates and train spiking neural networks without surrogate functions.
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