Functional mapping with complex higher order compensatory neuron model

The basic ideas to develop artificial neural network (ANN) were originated with the investigation of brain's micro-structure. It has been a steady endeavor in the research that followed to develop it further and integrate additional discoveries about the human brain with a view to evolve the artificial neuron model closer to the actual brain functioning. The pursuit has ever been on to replicate the typical characteristic of the neuron. The neuron response to the input signals impinged onto it, is defined how they are aggregated with in the unit. A substantial body of evidence has grown to support the presence of non-linear integration of synaptic inputs in the neuron cells. Superior functionality of ANN in complex domain has been observed in recent researches, which presented the second generation of development in ANN. In this paper, we explore the functional capabilities of a compensatory neuron model with complex-valued high order non-linear aggregation function. The strength and effectiveness of considered neuron is evaluated with an efficient learning algorithm in a complex domain. The performance analysis is carried out through a solid set of simulations.