  Computations on the dendritic trees of neurons have important constraints.
Voltage dependent conductances in dendrites are not similar to arbitrary
direct-current generation, they are the basis for dendritic nonlinearities and
they do not allow converting positive currents into negative currents. While it
has been speculated that the dendritic tree of a neuron can be seen as a
multi-layer neural network and it has been shown that such an architecture
could be computationally strong, we do not know if that computational strength
is preserved under these biological constraints. Here we simulate models of
dendritic computation with and without these constraints. We find that
dendritic model performance on interesting machine learning tasks is not hurt
by these constraints but may benefit from them. Our results suggest that single
real dendritic trees may be able to learn a surprisingly broad range of tasks.
