DOI: 10.5281/zenodo.18449947
title: Pyramidal Hybrid Neural Network Framework (BrainIAc_v2.0) : Technical Documentation and Experimental Results

description: Project Overview

This project validates the design, numerical stability, and learning dynamics of a hybrid neuronal network framework. Experiments cover eligibility traces, threshold adaptation, refractory dynamics, excitability bias, and learning rules across diverse neuron families, including LIF, Izhikevich, and Hodgkin-Huxley models.

The framework ensures biophysically plausible dynamics, stable learning, and modularity for downstream plasticity and task-level learning experiments.

Phase 1 — Eligibility Trace Validation

I. Single Spike Trace Validation (experiment_trace_single_spike.c)Objective: Validate correctness of eligibility trace dynamics to a single spike.





Tested across: LIF, Izh (RS, IB, CH, FS, LTS), HH; e = 5, 10, 20 ms




Results: Traces exhibit correct impulse response, exponential decay matches theory, baseline relative error <1%




Verdict: Formally validated, invariant, numerically stable



II. Superposition Validation (experiment_trace_superposition.c)Objective: Test linear superposition under multiple spikes





Results: Peak amplitudes ±1% of theory; accumulation and decay preserved; dt = e/100 ensures <1% error




Verdict: LTI behavior confirmed



III. Baseline Stability (experiment_trace_baseline.c)Objective: Ensure zero baseline and no drift





Results: Max and mean trace = 0 over long simulations (up to 500k steps)




Verdict: Stable, no spontaneous activity



IV. Heterogeneous Neuron Presets (experiment_trace_heterogeneous_all_neuron_presets.c)Objective: Validate independence from neuron/synapse type





Scope: 18 neuron presets, 4 synapse types, e = 5,10,50,100 ms




Results: Traces identical across presets (R²=1.0); synapse dynamics do not alter eligibility decay




Verdict: Fully decoupled, production-ready



Phase 2 — Post Spike Threshold Drift & Plastic Stability

I. Threshold Drift (experiment_threshold_drift.c)





Mean firing rates stable across inputs; adaptive thresholds converge




Long-term (60s) drift negligible (<2.91 Hz)




Recovery from induced silence complete (<106 ms)




Verdict: Threshold adaptation safe for learning



II. Refractory Drift (experiment_refractory_drift.c)





LIF, Izh, HH variants maintain stable ISI statistics




Long-term simulations: CV ≤2%, drift ≤2%




Max firing rates bounded by intrinsic dynamics




Failures: configured t_ref ≠ enforced min ISI; adaptive refractory sometimes fails recovery




Verdict: Stable, bounded, drift-free, with architectural clarifications needed



III. Excitability Bias Detection (experiment_excitability_bias_detection_all_neuron_presets.c)





LIF adaptive/refractory: strong, sometimes irreversible adaptation → task-gating required




Izh & HH: mild, fully reversible → plasticity safe




Classification: None, Mild, Strong adaptation




Verdict: Clear taxonomy; strong adapters require reset or gating



Learning Rule Experiments

I. Frozen Weight Control (experiment_frozen_weight_control_all_neuron_presets.c)





Normal training: learning occurs only where gradient informative (e.g., Izh RS +160%)




Frozen weights: ∆W = 0, performance flat




Plateau behavior in LIF/HH expected due to task saturation




Conclusion: Learning is causally weight-mediated, neuron-agnostic



II. Learning Rate Sensitivity (experiment_learning_rate_sensitivity_all_presets.c)





LIF: 4/5 stable; standard LIF unstable at all LRs




Izh: 10/10 stable; optimal LR 10⁻³ → 5×10⁻³




HH: 3/3 stable; conservative LR ~5×10⁻⁴




Gradient explosions/vanishing predictable; neuron-specific stability windows validated



III. Failed Learning Signals (experiment_frozen_weight_control.c)





Learning initially absent → signal misaligned




Fixed sign in e-prop: traces->learning_signals[i] = gamma * (actual_rates[i] - target_rates[i]);




Post-fix: all neuron families behave consistently; frozen control remains zero



Neuron-Specific Learning Summary








Neuron Preset
Learning Outcome
Notes




LIF Standard
Plateau
Already near rate ceiling


LIF Adaptive/Refractory
Weak/gradual
Requires careful LR


Izh RS
Strong (+160%)
Broad f-I curve, effective gradient


Izh FS
Weak
Steep f-I, saturated


HH Cortical
Minimal
Biophysically dominated, conservative learning






Component Mapping








Component
Mechanism
Memory
Global Signal




Gradient descent
∂L/∂w
None
Loss


Hebbian
x·y
Short
None


STDP
Spike timing
Explicit
None


Eprop
Eligibility trace
Explicit
Error / reward


Policy Gradients
∇logπ
Explicit
Reward prediction error


Local Sensitivity
∂L/∂w
Short
None






Key Takeaways





Eligibility traces: validated, neuron-agnostic, decoupled from synapse dynamics




Thresholds & refractory: stable, bounded, reversible; strong adaptation neurons require gating




Learning rules: plasticity-causal; frozen-weight controls confirm correct isolation




Learning rate sensitivity: predictable across neuron families; conservative LR for HH, higher tolerated for adaptive LIF




Pipeline detects hidden excitability/refractoriness biases → prevents unsafe plasticity



Final Validated Results: 15/18 (83.3%) ✅

Summary by Family








Family
Valid / Total
Converged / Total
Notes




LIF
4 / 5 (80%)
2 / 5
EIF unsuitable


Izhikevich
8 / 10 (80%)
4 / 10
TC, RZ unsuitable


Hodgkin-Huxley
3 / 3 (100%)
0 / 3
All valid, none converged






Validated Presets (15)








Preset
Reduction / Status




LIF Standard
100.0% ✅ CONVERGED


LIF Refractory
73.1% ✅ VALID


LIF Adaptive
91.0% ✅ VALID


LIF AdEx
100.0% ✅ CONVERGED


Izh RS
100.0% ✅ CONVERGED


Izh IB
99.9% ✅ CONVERGED


Izh CH
54.3% ✅ VALID


Izh FS
71.6% ✅ VALID 


Izh LTS
63.0% ✅ VALID


Izh AC
100.0% ✅ CONVERGED


Izh DS
63.0% ✅ VALID


Izh LTSp
68.2% ✅ VALID


HH Squid
53.2% ✅ VALID


HH Cortical
86.2% ✅ VALID 


HH Fast
94.5% ✅ VALID






Presets Requiring Specialized Training (3)








Preset
Issue
Recommendation




LIF EIF
Spikes but no learning
Custom surrogate gradient


Izh TC
Bistable dynamics
FORCE / reservoir computing


Izh RZ
Resonant (negative d)
Oscillation-based loss






 

**CC BY-NC-ND 4.0 — Attribution-NonCommercial-NoDerivatives 4.0 International**✅ Academic research and educational use freely permitted with proper attribution❌ Commercial use NOT permitted without explicit written permission📧 For commercial licensing inquiries: theo.vallois@hotmail.fr

**Source Code:**Full source code (BrainIAc framework) will be released under CC BY-NC-ND 4.0 on GitHub upon paper acceptance (expected Q1 2026).Repository: https://github.com/EmpireStrikesBack/NeuroModel

**Demonstration Video:**https://youtu.be/kiU609iozFwReal-time 3D visualization of 308-neuron pyramidal network with interactive controls.

**Contact:**Author: Théo ValloisEmail: theo.vallois@hotmail.frGitHub: https://github.com/EmpireStrikesBack/NeuroModel

**Total Archive Size:** ~23.5 MB (48 PDFs documentation)

**This archive serves as:**1. Intellectual property protection (timestamp)2. Pre-publication technical documentation  3. Complete reproducibility dataset (raw CSVs)4. Validation results for peer review