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A geometric model for cognition: the Fundamental Code Unit of the Brain

A geometric model for cognition: the Fundamental Code Unit of the Brain

The multidisciplinary nature of cognitive research brings the need to conceptually unify insights from multiple fields into the phenomena that drive cognition. Newton Howard and Amir Hussain propose the Fundamental Code Unit (FCU) as a means to better quantify the intelligent thought process at multiple levels of analysis and in order to model the brain’s most sophisticated decision-making process as efficiently as possible.

The study uses a number of case studies and applications of the human brain to demonstrate the mapping of physical processes observed at the chemical level to cognitive changes manifested through behavior (language abilities, linguistic semantics, etc.). They also investigate the composition of cognition at philosophical, psychological, and neurochemical levels. Considering these concepts as fundamental elements for a “coherent communicated thought”, the revelation of their network and connections can establish a universal cognitive code.

The Fundamental Code Unit of Thought (FCU) is an attempt to bridge the gap between observed physical phenomena and their complicated outcomes. Because cognition is both a physical and a computational (i.e., conceptual) phenomenon, any benchmark that is used to assess it must account for both.

The underlying units that compose cognition, like those of DNA, are relatively simple compared with the structures they create. This applies both to the brain itself and the way we perceive it (i.e., as a system of sensory inputs and linguistic and behavioral outputs). In our approach, we map the physical phenomena of cognition to this theoretical system.

Howard Newton, Brain language: the fundamental code unit.

A process-level view of the Fundamental Code Unit encoding within the brain. © Howard Newton, Amir Hussain

The first problem addressed by the team is the mechanisms of neurological oscillation and protein deformities (that cause various cognitive disorders). Since a protein’s structure typically provides the basis for its function, biologically important molecular events often consist of a change in the shape or configuration of key proteins. And, since each of these processes introduces the possibility of damaged misfolding, a prediction model can be created with FCU in order to establish which foldings and misfoldings serve as the correct or defective signal.

Further, to the uncertain and complex structure of cognition that influences all neural activity, The Maximum Entropy model is used. The statistical model uses machine learning techniques to forecast the chance of discovering something under specific conditions scattered across space using empirical data. One of the problems that FCU seeks to resolve in this context, is the lack of a common language to discern exactly what a neuro-mathematical model is, in terms of its capabilities and intended applications.

 Apart from its intuitive network configuration that resembles the structure of cognition itself, the computational power of the FCU lies in its ability to create a “database” of concept, physical process, and linguistic linkages that tend to occur at given mind states. 

One important metric explored in their language is “distance”, which helps to determine which conceptual components to cluster as information regarding tendencies is being gathered. Giving the Rosenfeld model (1996) as an example, the authors define a set of concepts, brain regions, and mappings between related concepts in each brain region identified in empirical studies. Upon these, the concept set framework is built to analyze the brain regions and their neural networks. The FCU’s task is to combine multiple sensor data streams into a single computationally efficient framework, such as linguistic input, neurological data (i.e., cell and network activation, and neural firing rate and amplitude), and behavioral phenomena (i.e., nervous tics, spatial judgment errors, and gait irregularities).

Being an expression of behavior, the team also uses language as a method to measure the transition from the molecular to the behavioral expressions of interactions of brain functions. Since the processes leading to the acquisition of language are distinct, conceptual divisions can provide important patterns and data. For this, an Axiological model (that uses value and value theory) is used to create a distinct code.

In order to resolve the dichotomy problem that arises from value attribution, they create a mapping of the axiological value to neurological state changes. This creates a clearer picture of the components and structure of cognition. The authors state that the interpretation of value only through dichotomy contradicts the notion that language’s use extends beyond its semantic properties and functions, and ignores the fact that we have a variety of construal apparatuses. They develop a time orientation schema, where “positive” can be analogous to “future-oriented” and “negative” can be analogous to “past-oriented.”

For example, when the inherent value of a word is unclear, it is helpful to consider the word’s antonym to determine its value. To determine the axiological value of linking and auxiliary verbs, the proposed model uses the “temporal value” of words based on the past-future scale. Future tense verbs convey a forward-looking attitude and should be developed in a positive way. Past tense verbs convey a backward perspective and should be marked as negative.

See Also

In addition to their conception of brain language, the team proposes a mathematical framework in which FCU is located: the Unitary System, founded on unary mathematics. The functions “unary plus” (+) and “unary minus” (), which express an increase or decrease in the underlying measured value, are computationally efficient enough to simulate human cognition, as long as both sides use the same linguistic foundation. For example, the unitary system can be seen functioning at the molecular level in the molecular chirality1In chemistry, a molecule or ion is called chiral if it cannot be superposed on its mirror image by any combination of rotations, translations, and some conformational changes. This geometric property is called chirality.concept.

The brain’s unitary operators carry a state of time and space that contains the information needed to decipher any semantic or non-semantic language used by the brain. When the FCU is deployed, these operators create a common language of cognition because they are language agnostic.

This position paper has outlined how using unary mathematics, and unifying external data with internal processes, can help achieve the outcome of pertinent thoughts in opposite situations—the most complex decision making process performed by the brain. Current and future research is aimed at experimentally testing and validating the FCU, for further setting the foundation for future hypothesis found in contemporary research and the theories underlying it.

The Fundamental Code Unit of the Brain: Towards a New Model for Cognitive Geometry, Newton Howard, Amir Hussain

Published: January 2018
DOI: 10.1007/s12559-017-9538-5


  • 1
    In chemistry, a molecule or ion is called chiral if it cannot be superposed on its mirror image by any combination of rotations, translations, and some conformational changes. This geometric property is called chirality.
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