Mathematics

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.