The brain is first and foremost a control system that is capable of building an internal representation of the external
world, and using this representation to make decisions, set goals and priorities, formulate plans, and control behavior
with intent to achieve its goals.
The computational model proposed here assumes that this internal representation resides in arrays of cortical columns.
More specifically, it models each cortical hypercolumn together with its underlying thalamic nuclei as a Fundamental
Computational Unit (FCU) consisting of a frame-like data structure (containing attributes and pointers) plus the
computational processes and mechanisms required to maintain it.
In sensory-processing areas of the brain, FCUs enable segmentation, grouping, and classification. Pointers stored in
FCU frames link pixels and signals to objects and events in situations and episodes that are overlaid with meaning and
emotional values. In behavior-generating areas of the brain, FCUs make decisions, set goals and priorities, generate
plans, and control behavior. Pointers are used to define rules, grammars, procedures, plans, and behaviors.
It is suggested that it may be possible to reverse engineer the human brain at the FCU level of fidelity using nextgeneration
massively parallel computer hardware and software.
Key Words: computational modeling, human cortex, brain modeling, reverse engineering the brain, image processing,
perception, segmentation, knowledge representation