  We present a new paradigm in the study of brain mental dynamics on the basis
of the stable transient activity neural networks observed in experiments. This
new approach is in contrast to traditional system analysis usually adopted in
cognitive modeling. Transient dynamics offers a sound formalism of the observed
qualities of brain activity, while providing a rigorous set of analysis tools.
Transients have two main features: First, they are resistant to noise, and
reliable even in the face of small variations in initial conditions; the
sequence of states visited by the system (its trajectory), is thus structurally
stable. Second, the transients are input-specific, and thus convey information
about what caused them in the first place. This new dynamical view manifests a
rigorous explanation of how perception, cognition, emotion, and other mental
processes evolve as a sequence of activity patterns in the brain, and, most
importantly, how they interfere with each other. The ideas discussed and
demonstrated here lead to the creation of a quantitative theory of the human
mental activity and can be deployed on artificial agents.
