AUTHOR=Tsien Joe Z. TITLE=Principles of Intelligence: On Evolutionary Logic of the Brain JOURNAL=Frontiers in Systems Neuroscience VOLUME=9 YEAR=2016 URL=https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2015.00186 DOI=10.3389/fnsys.2015.00186 ISSN=1662-5137 ABSTRACT=

Humans and animals may encounter numerous events, objects, scenes, foods and countless social interactions in a lifetime. This means that the brain is constructed by evolution to deal with uncertainties and various possibilities. What is the architectural abstraction of intelligence that enables the brain to discover various possible patterns and knowledge about complex, evolving worlds? Here, I discuss the Theory of Connectivity–a “power-of-two” based, operational principle that can serve as a unified wiring and computational logic for organizing and constructing cell assemblies into the microcircuit-level building block, termed as functional connectivity motif (FCM). Defined by the power-of-two based equation, N = 2i−1, each FCM consists of the principal projection neuron cliques (N), ranging from those specific cliques receiving specific information inputs (i) to those general and sub-general cliques receiving various combinatorial convergent inputs. As the evolutionarily conserved logic, its validation requires experimental demonstrations of the following three major properties: (1) Anatomical prevalence—FCMs are prevalent across neural circuits, regardless of gross anatomical shapes; (2) Species conservancy—FCMs are conserved across different animal species; and (3) Cognitive universality—FCMs serve as a universal computational logic at the cell assembly level for processing a variety of cognitive experiences and flexible behaviors. More importantly, this Theory of Connectivity further predicts that the specific-to-general combinatorial connectivity pattern within FCMs should be preconfigured by evolution, and emerge innately from development as the brain’s computational primitives. This proposed design-principle can also explain the general purpose of the layered cortex and serves as its core computational algorithm.