  The recently proposed network model, Operational Neural Networks (ONNs), can
generalize the conventional Convolutional Neural Networks (CNNs) that are
homogenous only with a linear neuron model. As a heterogenous network model,
ONNs are based on a generalized neuron model that can encapsulate any set of
non-linear operators to boost diversity and to learn highly complex and
multi-modal functions or spaces with minimal network complexity and training
data. However, the default search method to find optimal operators in ONNs, the
so-called Greedy Iterative Search (GIS) method, usually takes several training
sessions to find a single operator set per layer. This is not only
computationally demanding, also the network heterogeneity is limited since the
same set of operators will then be used for all neurons in each layer. To
address this deficiency and exploit a superior level of heterogeneity, in this
study the focus is drawn on searching the best-possible operator set(s) for the
hidden neurons of the network based on the Synaptic Plasticity paradigm that
poses the essential learning theory in biological neurons. During training,
each operator set in the library can be evaluated by their synaptic plasticity
level, ranked from the worst to the best, and an elite ONN can then be
configured using the top ranked operator sets found at each hidden layer.
Experimental results over highly challenging problems demonstrate that the
elite ONNs even with few neurons and layers can achieve a superior learning
performance than GIS-based ONNs and as a result the performance gap over the
CNNs further widens.
