  Evolution and its intelligence element present thrill and challenges in its
exploration. Yet, how species have memory, retrieve them and maintain
continuity are the fundamental questions. Most of the phenomenon can only be
hypothesised by researchers and validating them through experiments is a big
challenge. Taking brain as an ideal intelligent machine and modelling it opens
new dimensions for computational algorithm. This paper presents a hypothesis to
resemble memory creation in cerebral cortex. The regions of cerebral cortex are
implicit to be specific for specific function and constitute neural field that
is single dimension and have vector form. The neural field throughout cortex
connects with each other to form a network. These networks associate with
survival instincts, emotions and rewards to constitute a memory of the exposed
environment or say learning. Graphical tool NURBS with multidimensional control
points are implicitly used in representing these networks as a set of cubic
equations. Learning through data is a primary block of intelligent system, the
paper attempts to convert the data in lower dimension patterns rather than
existing absolute form for real time intelligent systems.
