  The work relates to a new way for analysis of one-dimensional stochastic
systems, based on consideration of its higher order difference structure. From
this point of view, the deterministic and random processes are analyzed. A new
numerical characteristic for one-dimensional stochastic systems is introduced.
The applications to single neuron models and neural networks are given.
