Something I ignored, when I worked with SNN was, that dendrites play an active part in the signal processing (are non-linear). SNN equations have been developed by building a model of the cell membrane of a neuron. Therefore they can model very accurately, how the voltage and other parameters in the neuron change.
There are two different approaches, how SNN can be used in models:
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On one hand, people try to build very accurate and complex models of neurons, where they simulate the whole structure (including the branching of dendrites) of the neuron. This approach allows to have a very accurate models of neurons. The price is, that these models are very very computationally expensive!
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Another approach is to simulate just a single neuron (speaking in terms of the equations: just a single membrane point for each neuron). This is of course much less computationally expensive, however the possibility to integrate the non-linearity of dendrites is not possible any more. For these type of SNN there exist a lot of different hardware accelerators (like SpiNNaker, introduced in the post before).
It might be enough for some research to just simulate a few neurons with high accuracy, or to simulate much more neurons at a lower accuracy. But because we do not have unlimited computational power, we must build our models so that only necessary concepts are built into the algorithm. But the question is, what is a necessary concept?
HTM does not try to model every ion-channel of the cell membrane and so can not give you information about the voltage of cell. So HTM ignores some biological implementation details to save computational effort. But HTM is aware of important concepts and tries for example to model the non-linearity of dendrites.