Classical conditioning implementation with realneuron networks

This paper presents a contribution to computational modelling of associative learning through classical conditioning as known in the psychology. A seven-neuron RealNeuron network model, derived from neurobiological descriptions, and its performance is presented, using multiple resolution levels with configurable modular elements at each resolution level. RealNeurons are based on the structure of a biological neuron. With the RealNeuron's simple calculations, simulations on personal computers are possible and the simulated states on the highest, intermediate and lowest levels of resolution can be calculated using standard spreadsheet software. Further, a synthesis of a complex system, using an eleven-neuron RealNeuron network and integrates two classical-conditioning functions that can adapt to a changing poison and food environment, and its performance is presented.