  Neural network models generally involve two important components, i.e.,
network architecture and neuron model. Although there are abundant studies
about network architectures, only a few neuron models have been developed, such
as the MP neuron model developed in 1943 and the spiking neuron model developed
in the 1950s. Recently, a new bio-plausible neuron model, Flexible Transmitter
(FT) model, has been proposed. It exhibits promising behaviors, particularly on
temporal-spatial signals, even when simply embedded into the common feedforward
network architecture. This paper attempts to understand the properties of the
FT network (FTNet) theoretically. Under mild assumptions, we show that: i)
FTNet is a universal approximator; ii) the approximation complexity of FTNet
can be exponentially smaller than those of commonly-used real-valued neural
networks with feedforward/recurrent architectures and is of the same order in
the worst case; iii) any local minimum of FTNet is the global minimum, implying
that it is possible to identify global minima by local search algorithms.
