Neural Information Organizing and Processing -- Neural Machines
This work addresses the challenge of unified modeling for neural systems, which is incremental as it builds on existing concepts to improve descriptions and implementations.
The paper tackles the problem of modeling neural systems by proposing a unified informational framework called neural machines, which integrates parameters like neural power to describe both natural and artificial systems, resulting in a model that overcomes technical constraints in artificial implementations and provides a more relevant description of natural processes.
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.