The field of Computational Neuroscience is where neuroscience and computational modelling merge together. It is an ever-emerging area of research where the level of biological modelling can range from small-scale cellular models, to the larger network scale models. This MSc Thesis will detail the research carried out when looking at a small network of two neurons. These neurons have been modelled with a high level of detail, with the intention of using it to study the phenomenon of Spike-Timing-Dependent Plasticity (or STDP). Spike-Timing-Dependent Plasticity is the occurrence of either a strengthening or weakening in connection between two neurons, depending on the temporal order of stimulation between them. A major part of the work detailed is the focus on what mechanisms are responsible for these changes in plasticity, with the goal of representing the mechanisms in a single learning rule. The results found can be directly compared to data previously seen by scientists who worked on in-vitro experiments. The research then goes on to look at further applications of the model, in particular, looking at certain deficits seen in people with Schizophrenia. We modify the model to include these cellular impairments, then observe how this affects the standard STDP curve and thus affects the strengthening/weakening between the two neurons
