To determine which inputs for a neuron are important and which information a neuron should listen to is
an important problem during brain development and during learning. Spike-Timing Dependent Plasticity
(STDP) is a physiological adaptation mechanism of synaptic regulation which make a neuron to determine
which neighboring neurons are worth by potentiating those inputs and depressing the other. We work on
obtaining a good mathematical understanding of Spike-Timing Dependent Plasticity (STDP). This involves
understanding why STDP works so well and the signicance of factors like STDP type, learning window
and scaling under increasing network size. The mathematical model we construct using phase oscillators
is a good example of discrete adaptive asynchronous network. This is a joint work with Mike Field from
Rice University.