  Research on neuromorphic computing is driven by the vision that we can
emulate brain-like computing capability, learning capability, and
energy-efficiency in novel hardware. Unfortunately, this vision has so far been
pursued in a half-hearted manner. Most current neuromorphic hardware (NMHW)
employs brain-like spiking neurons instead of standard artificial neurons. This
is a good first step, which does improve the energy-efficiency of some
computations, see \citep{rao2022long} for one of many examples. But current
architectures and training methods for networks of spiking neurons in NMHW are
largely copied from artificial neural networks. Hence it is not surprising that
they inherit many deficiencies of artificial neural networks, rather than
attaining brain-like functional capabilities.
  Of course, the brain is very complex, and we cannot implement all its details
in NMHW. Instead, we need to focus on principles that are both easy to
implement in NMHW and are likely to support brain-like functionality. The goal
of this article is to highlight some of them.
