Feb 28, 2022
Mathematics in Mind: Modeling Human Learning Through Systems Biology
Category: Neuroscience & Learning Science | Reading Time: 8 min | By: NYI Editorial Team
If fingerprints are the map and the brain is the landscape, then mathematics is the language that explains both.
Every habit, emotion, or insight you’ve ever had was once a pattern — a small equation in motion.
The Mathematics of Life
In nature, patterns don’t appear by accident.
From the veins on a leaf to the folds of the human cortex, they arise from reaction–diffusion systems, first modeled by mathematician Alan Turing in 1952.
His idea was simple but revolutionary: when two substances spread and interact at different speeds, they create stable, self-repeating patterns.
In the human embryo, these same dynamics shape both skin ridges (as proven in Cell, 2023 ↗) and neural architectures (as modeled in Briefings in Bioinformatics, 2024 ↗).
The math that governs ridges also governs reasoning.
“Every neural pathway is a living equation — solved and re-solved with every thought.”
— NYI Research Team
How the Brain Computes Itself
The developing brain operates like a nonlinear dynamic system — thousands of feedback loops adjusting continuously to minimize error.
This mirrors how machine learning optimizes weights and biases through iterative correction.
The Doshi-Velez & Kim (2017) paper “Towards a Rigorous Science of Interpretable Machine Learning” ↗ highlights how transparency in AI models helps decode complex systems.
Likewise, understanding the mathematics of the brain helps us interpret human learning — to see why someone learns the way they do.
At NYI, our modeling framework borrows from these same principles.
We treat intelligence not as a static score, but as an evolving algorithm:
Potential = Pattern × Adaptation ÷ Resistance.
From Equations to Experience
Systems biology explains that biological intelligence emerges from constraint satisfaction — the balance between energy, information, and feedback.
When a student solves a math problem, remembers a melody, or learns from failure, their neural system is literally running a model — adjusting to optimize outcomes under uncertainty.
The OECD (2022) report “Understanding Student Well-Being in PISA 2022” ↗ validates this through data: well-being, motivation, and self-efficacy directly correlate with improved cognitive flexibility — the system’s ability to re-model itself.
This is why two children taught the same way can produce entirely different outcomes:
Their mathematical learning models are unique to their biology.
Modeling Learning: From Biology to Behavior
The National Academies (2018) book “How People Learn II” ↗ emphasizes that learning depends on three systems:
Memory (pattern storage)
Attention (pattern recognition)
Motivation (pattern reinforcement)
At NYI, our NeuroPrint™ translates these systems into data-driven insight models, helping parents and educators understand how a child’s brain “computes” learning.
The goal isn’t to predict, but to simulate — to build a digital twin of potential.
The NYI Equation
Learning Efficiency = (Curiosity × Feedback Loops × Emotional Stability) ÷ (Cognitive Friction × Time)
This proprietary conceptual model explains why NYI interventions reduce learning fatigue and increase retention: they optimize for clarity, not complexity.
By aligning learning systems with biological architecture, NYI helps children “solve themselves” — intuitively, not mechanically.
Science Layer
Cell (2023) — Developmental Basis of Fingerprint Pattern Formation ↗
Briefings in Bioinformatics (2024) — Embryonic Neurodevelopment ↗
Experience the Equation of Growth.
Discover how NYI’s NeuroPrint™ models intelligence as an adaptive, evolving system.
Explore the Science →
