Iterative temporal differencing with fixed random feedback alignment support spike-time dependent plasticity in vanilla backpropagation for deep learning

Aras Dargazany, Kunal Mankodiya

Abstract:

In vanilla backpropagation (VBP), activation function matters considerably in terms of non-linearity and differentiability.
Vanishing gradient has been an important problem related to the bad choice of activation function in deep learning (DL).
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 (ITD) using fixed random feedback weight alignment (FBA).
Using FBA with ITD, we can transform the VBP into a more biologically plausible approach for learning deep neural network architectures.
We don't claim that ITD works completely the same as the spike-time dependent plasticity (STDP) in our brain but this work can be a step toward the integration of STDP-based error backpropagation in deep learning.

TL;DR: Iterative temporal differencing with fixed random feedback alignment support spike-time dependent plasticity in vanilla backpropagation for deep learning.

Keywords: Iterative temporal differencing, feedback alignment, spike-time dependent plasticity, vanilla backpropagation, deep learning