 There has been a cottage industry in developing more biologically-plausible algorithms for error-driven training that can be shown to approximate the results of backpropagation without its implausible features. Prominent examples include O’Reilly’s Generalized Error Recirculation algorithm (O’Reilly 1996), using randomized error signals rather than error signals individually computed for each neuron (Lillicrap, Cownden, Tweed, &amp; Akerman 2016), and modifying weights using spike-timing dependent plasticity--the latter of which has been a favorite of prominent figures in deep learning research (Bengio et al. 2017). (For more on deep learning see  section 11  below.) 