Professor Mike Field's research is mainly in dynamical systems and includes statistical properties (ergodic theory and mixing), network dynamics with special reference to asynchronous networks, adaptation, event driven dynamics and non-smooth dynamics, and the geometric theory of dynamical systems with symmetry and the mechanisms whereby symmetry can lead to complex dynamics in low dimensional systems. Areas of application include spiking neuronal networks, plasticity and visualization. Currently, he is working on problems in Artifical Neural Nets (joint work with Yossi Arjevani).