I wouldn't really say that. There's a lot of progress being made in using and understanding biological processes for useful tasks. Not surprisingly, the biological mechanisms are surprisingly complex and rich and vary a lot through the brain (like you mentioned with the dendrites. Dendrites also work like coincidence detection mechanisms sometimes in some layers of the cortex [1] for instance)
[2] gives a very nice overview of what is needed from both machine learning and computational neuroscience to solve the entire problem of understanding the human brain.
There was the liquid state computing paper [3] in 2002 which showed that random networks of spiking neurons can perform some computations and have memory even in the absence of special learning rules.
There has also been quite some work on understanding e.g. the role of assemblies of neurons (groups of neurons firing), assembly sequences, plasticity rules, theory formulating neural activity in a probabilistic manner, a better understanding of the role of inhibition, dendrites etc.
And lastly, there have been huge advances in neuromorphic computing, both digital and analog. In some cases, the performance on these chips (that use spiking neurons) approaches that of state of the art machine learning. e.g. [4]
[1] http://www.sciencedirect.com/science/article/pii/S0166223612...
[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5021692/
[3] http://www.mitpressjournals.org/doi/abs/10.1162/089976602760...