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. 2016 Feb 3:9:186.
doi: 10.3389/fnsys.2015.00186. eCollection 2015.

Principles of Intelligence: On Evolutionary Logic of the Brain

Joe Z Tsien  1
Affiliations

Principles of Intelligence: On Evolutionary Logic of the Brain

Joe Z Tsien. Front Syst Neurosci. .

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 = 2 (i) -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.

Keywords: artificial intelligence; brain evolution; cell assembly; computational logic; functional connectivity motif; neural cliques; origin of intelligence; theory of connectivity.

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Figures

Figure 1
Figure 1
Evolutionary logic guiding brain’s wiring and computation at the cell-assembly level. (A) Examples of connectivity patterns within representative FCMs. On the left, a simple functional connectivity motif (FCM) with only three neurons for covering two distinct inputs (in red or yellow, i1 or i2, respectively). In the middle, a FCM consists of 15 distinct neural cliques (n1–5), which cover all possible connectivity patterns in order to process four distinct inputs (i1–4) by following the specific-to-general combinatorial wiring and computational logic. The proposed FCMs are predicted to be the cell-assembly level building blocks for constructing various brains (e.g., mouse, dog, and human are illustrated here). (B) Universality of this computational logic, which can be detected by measuring neural activation patterns in the form of a “bar-code”. Warm color bars illustrate the activations of these cell cliques. Schematic “bar-code” illustration of specific-to-combinatorial input connectivity motifs are predicted to be present in a wide range of brain regions regardless of anatomical shapes. Three bar codes all contain FCMs for processing four distinct inputs (i = 4), each consists of 15 distinct neural cliques (n1–15). (C) On the right, a “cognitive example” is provided for how the bar code-like activation patterns of FCMs give rise to a specific-to-general feature-extraction assembly that encodes specific features or memories, as well as various relational or generalized knowledge about four distinct fearful events. This specific-to-general computational logic can also be used to generate categorical motor behaviors.
Figure 2
Figure 2
Illustration of experiments to test this neural clique assembly-based computational logic. (A) Specific-to-general neural cliques in the mouse CA1 region. A total of 757 CA units from five mice (n = 189 ± 29) were pooled together to generate this hierarchical clustering plot. Some cells responded to all three fearful stimuli (general clique), while some cell cliques (specific or sub-general cells) exhibited specific or sub-combinatorial selectivity to one or two types of stimulus, respectively. (B) Specific-to-general neural cliques in the mouse anterior cingulate cortex (ACC) region in responding three distinct fearful events. A total of 682 ACC units from six mice (n = 137 ± 43) were pooled together to generate this hierarchical clustering plot. The distinct fearful events are labeled. The CA1 and ACC figures were adopted from Lin et al. (2005) and Xie et al. (2013), respectively. The color scale bar indicates the Z-score normalized magnitude in firing changes within 2 s after stimulus onset.
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