||The study of animal learning has become an important aspect of machine learning research. Many recent studies have focused on implementing both psychological and biological learning models in computer systems to investigate the cognitive aspects of learning in animals. According to different experimental results, the synaptic strength between the "trained" neurons is increased. However, it remains unclear whether increase in synaptic strength is the principal modification in learning. Although change in synaptic strength is the most obvious alternation from the learning process, it is not unreasonable to believe that the modification of temporal relationship between the applied stimulus and generated responses is also an important factor. In fact, recent studies on Long-Term Potentiation (LTP), a physiological learning mechanism recorded in mammals [l0], have shown that the temporal relationship between stimulus and response is both affected by and affecting the LTP mechanism . In this thesis, we demonstrate that LTP affects the synaptic coding process in neurons (the transformation of inputs into outputs via the synapse). By implementing a general LTP mathematical model  on our existing neuron simulator [85, 92], we show that LTP modifies the behavioral responses of the neuron. This includes an increase in the degree of synchronization and the clustering of such synchronizations into a smaller number of phase-locking groups. Finally, we propose that LTP simplifies the synaptic code produced by neurons after learning.