  The informational synthesis of neural structures, processes, parameters and
characteristics that allow a unified description and modeling as neural
machines of natural and artificial neural systems is presented. The general
informational parameters as the global quantitative measure of the neural
systems computing potential as absolute and relative neural power were
proposed. Neural information organizing and processing follows the way in which
nature manages neural information by developing functions, functionalities and
circuits related to different internal or peripheral components and also to the
whole system through a non-deterministic memorization, fragmentation and
aggregation of afferent and efferent information, deep neural information
processing representing multiple alternations of fragmentation and aggregation
stages. The relevant neural characteristics were integrated into a neural
machine type model that incorporates unitary also peripheral or interface
components as the central ones. The proposed approach allows overcoming the
technical constraints in artificial computational implementations of neural
information processes and also provides a more relevant description of natural
ones.
