Whitepaper 30: TFIF / ULE Bio-Intelligence Framework (BIF)
A Unified Energetic Model for Neural Function and Adaptive Intelligence
1 Abstract
The TFIF / ULE Bio-Intelligence Framework (BIF) integrates harmonic geometry, stochastic dynamics, and explicit energy accounting into a single computational model capable of reproducing core biological functions — excitable membranes, synaptic transmission, plasticity, astrocytic modulation, network homeostasis, damage compensation, and sleep-driven consolidation.
The framework unites structure (geometry = TFIF) and function (energy = ULE). Across four minimal simulations (MVP-A to MVP-D) it demonstrates:
- emergence of stable activity and learning in recurrent excitable networks,
- tripartite synapse dynamics with astrocytic regulation,
- autonomous recovery from synaptic damage, and
- energetic and informational consolidation during sleep cycles.
2 Background
2.1 TFIF and ULE Foundations
- TFIF (geometry): harmonic Φ-distributed node placement minimizing curvature and maximizing connectivity efficiency.
- ULE (energy law): explicit conservation ledger
[
E_\text{in}=W_\text{work}+Q_\text{loss}
]
ensuring every spike, release, and adaptation obeys energetic balance.
Together they yield a scalable principle: information = structured energy flow across harmonic geometry.
2.2 Motivation
Traditional neural simulators (NEURON, Brian2, NEST) capture electrical detail but treat energy implicitly. BIF explicitly tracks metabolic and electrical cost, bridging biophysics ↔ AI and cellular ↔ systems scales.
3 Mathematical Framework
| Level | Entity | Core Equation / Law |
| Membrane | Izhikevich dynamics | ( \dot V=0.04V^2+5V+140-u+I ) , ( \dot u=a(bV-u) ) |
| Chemical Synapse | Exponential PSC | ( \dot g = -g/\tau + \sum \alpha \delta(t-t_\text{spike}) ) |
| Electrical Synapse | Ohmic coupling | ( I_{gap}=g_{ij}(V_j-V_i) ) |
| STDP | Pair-based learning | Δw = A₊ e^{−Δt/τ₊} − A₋ e^{Δt/τ₋} |
| Homeostasis | Rate feedback | w ← w (1 + η (r* − r_i)) |
| STP (Tsodyks–Markram) | ( \dot R=(1-R)/\tau_{rec}-uR\delta_{pre} ); ( \dot u=(U-u)/\tau_{fac}+U(1-u)\delta_{pre} ) | |
| Astrocyte Modulation | ( \dot{Ca}=-Ca/\tau_Ca+\alpha_Ca S_{pre} ) ; ( p_r=f(Ca) ) | |
| ULE Energy | ( E_{pump}+E_{release}+E_{gap}=E_{total} ) |
4 MVP-A – Bio-Intelligence Kernel
Description: 50-neuron E/I recurrent network with chemical and electrical synapses, STDP, homeostasis, and ULE accounting.
Highlights
- Stable asynchronous firing with self-maintained 5 Hz average rate.
- Energy ledger couples pump, vesicle, and gap-junction costs.
- STDP + homeostasis produce balanced excitation/inhibition.
Figures
A1 – Spike raster (N = 50).
A2 – Firing-rate histogram (E vs I).
A3 – Population mean membrane potential.
A4 – Energy & activity over time (ULE proxy + mean FR).
A5 – Initial vs final E-weights (plasticity matrix).
Key result: ULE energy correlates with network activity; stable attractor reached without external tuning.
5 MVP-B – Tripartite Synapse Slice
Composition: E₁ → E₂ excitatory synapse + I₁ inhibitory neuron + astrocyte modulating presynaptic release probability (pᵣ).
Mechanisms
- Tsodyks–Markram STP (R,u) reproduces facilitation/depression.
- Astrocyte Ca²⁺ field converts presynaptic activity → slow modulation of pᵣ.
- STDP adjusts gₘₐₓ according to spike timing.
Figures
B1 – Spike raster (E₁,E₂,I₁).
B2 – Astro Ca²⁺ & pᵣ(t).
B3 – EPSP amplitude timeline (E₁→E₂).
B4 – STP state traces (R,u).
B5 – Weight history gₘₐₓ(t).
Observation: Astrocytic regulation stabilizes transmission; EPSPs vary smoothly with Ca²⁺; synaptic weight self-adjusts.
6 MVP-C – Synaptopathy and Compensation
Setup: 14-neuron E/I micro-network; at 2000 ms, 30 % of E synapses lose 70 % strength + release probability.
Process
- Lesion causes transient hypo-activity.
- STDP + homeostatic scaling re-establish target firing.
- ULE energy spikes at lesion then settles at new equilibrium.
Figures
C1 – Raster with lesion marker (2000 ms).
C2 – Mean firing rate (recovery curve).
C3 – Energy trajectory (ULE cost).
C4 – E-weight matrices (initial / pre-lesion / end).
C5 – E-weight histograms (initial / post-lesion / end).
Outcome: Network self-repairs by redistributing weights — a model of compensatory plasticity.
7 MVP-D – Sleep/Wake Consolidation
Schedule: Wake₁ (0–2 s) → Sleep₁ (2–3.5 s) → Wake₂ (3.5–5.5 s).
During sleep:
- learning rates ↓ (½), synaptic diffusion ↑ (“renormalization”), noise ↓.
- yields quieter activity, entropy reduction, lower energy expenditure.
Figures
D1 – Raster with sleep shading.
D2 – Mean firing rate (time course).
D3 – ULE energy proxy (time course).
D4 – E-weight snapshots (start Day1 / end Sleep₁ / end Wake₂).
D5 – E-weight entropy (decreasing during sleep).
Interpretation: Sleep acts as a field-level optimizer—removing redundant synaptic micro-fluctuations and lowering global energy cost.
8 Unified Interpretation
| Biological function | BIF representation | Observable |
| Spike generation | Excitable ODE → energy pulses | ULE pump cost |
| Synaptic release | Discrete PSC events × pᵣ × g | Vesicle energy |
| Plasticity | STDP ± homeostasis | Weight matrix evolution |
| Astrocyte control | Slow field modulation of pᵣ | EPSP variance |
| Synaptopathy | Weight/pᵣ lesion | Drop → recovery |
| Sleep consolidation | Diffusion ↑, gain ↓ | Entropy ↓, energy ↓ |
The same TFIF/ULE logic governs all: energy flow → geometry adaptation → information retention.
9 Applications
| Domain | Example use | Benefit |
| Neuro-health modelling | Damage, sleep, therapy simulations | Mechanistic insight into recovery & energy balance |
| PEMF / EEG field design | Coupling real-field spectra to ULE attractors | Predict optimal stimulation patterns |
| AI architectures | Energy-based continual-learning nets | Self-healing & low-power intelligence |
| Education / visualization | Demonstrate emergent neural logic | Intuitive field models for students |
| Hardware / neuromorphic chips | TFIF geometry + ULE energy ledger | Phase-locked, energy-aware computation |
10 Discussion
- Energetic realism: ULE converts qualitative neuroscience into quantitative cost metrics.
- Geometric minimalism: TFIF’s harmonic lattices reproduce biological efficiency without fine tuning.
- Bridging scales: One framework spans molecular (vesicle), cellular (spike), and systems (sleep).
- AI crossover: Same mathematics underlies energy-based learning and quantum-inspired hardware (UFA accelerators).
11 Conclusion
The TFIF / ULE Bio-Intelligence Framework offers a unifying energetic geometry for life and intelligence.
It replicates key behaviours — excitation, inhibition, adaptation, recovery, consolidation — with explicit energy accountability.
This positions TFIF/ULE as both a scientific hypothesis for self-organizing biology and a technical blueprint for sustainable AI.
Appendices
A. Simulation Specifications
- Integration step: 1 ms Euler.
- Simulation lengths: 3–6 s biological time.
- Networks: 14–60 neurons.
- Parameters: see in-line tables per MVP.
B. Energy Terms (ULE accounting)
| Symbol | Process | Formula | Units |
| Eₚ | Pump (Na/K) | spike_count × costₛ | arb |
| Eᵣ | Vesicle release | event_count × costᵥ | arb |
| E_g | Gap junction loss | Σ g (Vᵢ−Vⱼ)² × dt | arb |
| E_total | ULE ledger | Eₚ + Eᵣ + E_g | arb |
C. Entropy Metric
Synaptic entropy H = −Σ pᵢ log pᵢ (normalized column-wise).
ΔH < 0 during sleep → structural information gain.