  One major criticism of deep learning centers around the biological
implausibility of the credit assignment schema used for learning --
backpropagation of errors. This implausibility translates into practical
limitations, spanning scientific fields, including incompatibility with
hardware and non-differentiable implementations, thus leading to expensive
energy requirements. In contrast, biologically plausible credit assignment is
compatible with practically any learning condition and is energy-efficient. As
a result, it accommodates hardware and scientific modeling, e.g. learning with
physical systems and non-differentiable behavior. Furthermore, it can lead to
the development of real-time, adaptive neuromorphic processing systems. In
addressing this problem, an interdisciplinary branch of artificial intelligence
research that lies at the intersection of neuroscience, cognitive science, and
machine learning has emerged. In this paper, we survey several vital algorithms
that model bio-plausible rules of credit assignment in artificial neural
networks, discussing the solutions they provide for different scientific fields
as well as their advantages on CPUs, GPUs, and novel implementations of
neuromorphic hardware. We conclude by discussing the future challenges that
will need to be addressed in order to make such algorithms more useful in
practical applications.
