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- W4288282364 abstract "This work shows that a differentiable activation function is not necessary any more for error backpropagation. The derivative of the activation function can be replaced by an iterative temporal differencing using fixed random feedback alignment. Using fixed random synaptic feedback alignment with an iterative temporal differencing is transforming the traditional error backpropagation into a more biologically plausible approach for learning deep neural network architectures. This can be a big step toward the integration of STDP-based error backpropagation in deep learning." @default.
- W4288282364 created "2022-07-28" @default.
- W4288282364 creator A5011305189 @default.
- W4288282364 date "2019-07-15" @default.
- W4288282364 modified "2023-09-26" @default.
- W4288282364 title "Iterative temporal differencing with random synaptic feedback weights support error backpropagation for deep learning" @default.
- W4288282364 doi "https://doi.org/10.48550/arxiv.1907.07255" @default.
- W4288282364 hasPublicationYear "2019" @default.
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