  The recently proposed Activation Relaxation (AR) algorithm provides a simple
and robust approach for approximating the backpropagation of error algorithm
using only local learning rules. Unlike competing schemes, it converges to the
exact backpropagation gradients, and utilises only a single type of
computational unit and a single backwards relaxation phase. We have previously
shown that the algorithm can be further simplified and made more biologically
plausible by (i) introducing a learnable set of backwards weights, which
overcomes the weight-transport problem, and (ii) avoiding the computation of
nonlinear derivatives at each neuron. However, tthe efficacy of these
simplifications has, so far, only been tested on simple multi-layer-perceptron
(MLP) networks. Here, we show that these simplifications still maintain
performance using more complex CNN architectures and challenging datasets,
which have proven difficult for other biologically-plausible schemes to scale
to. We also investigate whether another biologically implausible assumption of
the original AR algorithm -- the frozen feedforward pass -- can be relaxed
without damaging performance.
