Transformations of Neural Inputs in Lattice Dendrite Computation

摘要

In the present paper, lattice dendrite computation is extended with non-linear transformations of neural inputs that are applied before local discrimination is performed by each dendrite of an artificial neuron. At the expense of increasing the gap with biological analogies or biophysical similarities, the proposed mathematical extension to the basic single layer lattice perceptron model has the advantage that with appropriate input transformations one type synaptic connections can be used, excitatory or inhibitory only; similarly, a reduction in the number of dendrites needed to solve certain one-class recognition problems can be achieved. Illustrative examples are given to show the new capabilities and possible applications of this enhanced single layer lattice perceptron.