  We introduce a model for an artificial neuron which is based on ballistic
transport in a multi-terminal device. Unlike standard configurations, the
proposed design embeds the synaptic weights into the active region, thus
significantly reducing the complexity of the input terminals. This is achieved
by defining the basic elements of the ballistic artificial neuron as follows:
the input values are set by the incoming wavefunctions amplitudes, while the
weights correspond to the scattering matrix elements. Furthermore, the output
value of the activation function of the artificial neuron is given by the
transmission function. By tuning the gate voltage, the scattering potential
and, consequently, the weights are changed so that the value of the
transmission function gets closer to the target output, which is essential in
the training process of artificial neural networks. Thus, we provide here the
modus operandi of a ballistic artificial neuron.
