  The increasing interest in understanding the behavior of the biological
neural networks, and the increasing utilization of artificial neural networks
in different fields and scales, both require a thorough understanding of how
neuromorphic computing works. On the one side, the need to program those
artificial neuron-like elements, and, on the other side, the necessity for a
large number of such elements to cooperate, communicate and compute during
tasks, need to be scrutinized to determine how efficiently conventional
computing can assist in implementing such systems. Some electronic components
bear a surprising resemblance to some biological structures. However, combining
them with components that work using different principles can result in systems
with very poor efficacy. The paper discusses how the conventional principles,
components and thinking about computing limit mimicking the biological systems.
We describe what changes will be necessary in the computing paradigms to get
closer to the marvelously efficient operation of biological neural networks.
