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This work shows that a differentiable activation function is not necessaryany more for error backpropagation. The derivative of the activation functioncan be replaced by an iterative temporal differencing using fixed randomfeedback alignment. Using fixed random synaptic feedback alignment with aniterative temporal differencing is transforming the traditional errorbackpropagation into a more biologically plausible approach for learning deepneural network architectures. This can be a big step toward the integration ofSTDP-based error backpropagation in deep learning.

Iterative temporal differencing with random synaptic feedback weights support error backpropagation for deep learning