The most famous form of bio-inspired computing is
artificial neural networks.
In that case, the processing units are neurons and the dynamic interconnections are synapses.
But other degrees of granularity can be envisionned.
The bio-chemistry of
where computation is performed through cascade reactions at the molecular level,
is also an important source of inspiration. For example, allosteric molecules can be seen as processing units, and secondary
messengers as dynamic interconnections.
Probabilistic computing is another expression of bio-inspired computing.
Indeed, noise is often a key element of bio computation, beneficial for a number of operations as near-threshold signaling and
decision making. For instance stochastic resonance, a noise induced sensitivity improvement , exists at the neural level.
Stochastic binary behaviors are also often observed in cell signaling pathways: neurotransmitter release through the synaptic
cleft or the opening/closing of ionic channels are probabilistic biological processes. In general, stochastic computing trades
absolute accuracy against considerable energy gains.
In all cases, finding nanodevices that can emulate the processing units and dynamic connections directly
or through CMOS hybridation is a very suited solution to map these massively parallel systems on hardware.