  Current neural networks are mostly built upon the MP model, which usually
formulates the neuron as executing an activation function on the real-valued
weighted aggregation of signals received from other neurons. In this paper, we
propose the Flexible Transmitter (FT) model, a novel bio-plausible neuron model
with flexible synaptic plasticity. The FT model employs a pair of parameters to
model the transmitters between neurons and puts up a neuron-exclusive variable
to record the regulated neurotrophin density, which leads to the formulation of
the FT model as a two-variable two-valued function, taking the commonly-used MP
neuron model as its special case. This modeling manner makes the FT model not
only biologically more realistic, but also capable of handling complicated
data, even time series. To exhibit its power and potential, we present the
Flexible Transmitter Network (FTNet), which is built on the most common
fully-connected feed-forward architecture taking the FT model as the basic
building block. FTNet allows gradient calculation and can be implemented by an
improved back-propagation algorithm in the complex-valued domain. Experiments
on a board range of tasks show the superiority of the proposed FTNet. This
study provides an alternative basic building block in neural networks and
exhibits the feasibility of developing artificial neural networks with neuronal
plasticity.
