  We provide a brief review of the common assumptions about biological learning
with findings from experimental neuroscience and contrast them with the
efficiency of gradient-based learning in recurrent neural networks. The key
issues discussed in this review include: synaptic plasticity, neural circuits,
theory-experiment divide, and objective functions. We conclude with
recommendations for both theoretical and experimental neuroscientists when
designing new studies that could help bring clarity to these issues.
