  The human brain has immense learning capabilities at extreme energy
efficiencies and scale that no artificial system has been able to match. For
decades, reverse engineering the brain has been one of the top priorities of
science and technology research. Despite numerous efforts, conventional
electronics-based methods have failed to match the scalability, energy
efficiency, and self-supervised learning capabilities of the human brain. On
the other hand, very recent progress in the development of new generations of
photonic and electronic memristive materials, device technologies, and 3D
electronic-photonic integrated circuits (3D EPIC ) promise to realize new
brain-derived neuromorphic systems with comparable connectivity, density,
energy-efficiency, and scalability. When combined with bio-realistic learning
algorithms and architectures, it may be possible to realize an 'artificial
brain' prototype with general self-learning capabilities. This paper argues the
possibility of reverse-engineering the brain through architecting a prototype
of a brain-derived neuromorphic computing system consisting of artificial
electronic, ionic, photonic materials, devices, and circuits with dynamicity
resembling the bio-plausible molecular, neuro/synaptic, neuro-circuit, and
multi-structural hierarchical macro-circuits of the brain based on well-tested
computational models. We further argue the importance of bio-plausible local
learning algorithms applicable to the neuromorphic computing system that
capture the flexible and adaptive unsupervised and self-supervised learning
mechanisms central to human intelligence. Most importantly, we emphasize that
the unique capabilities in brain-derived neuromorphic computing prototype
systems will enable us to understand links between specific neuronal and
network-level properties with system-level functioning and behavior.
