Pyramidal Hybrid Neural Network Framework (BrainIAc_v2.1) : Technical Documentation and Experimental Results

Project Overview This project validates the design correctness, numerical stability, and learning scalability of a hybrid spiking neuronal network framework implementing e-prop–style local learning rules. The work systematically separates solver stability, biophysical dynamics, and learnability under local credit assignment. Experiments cover eligibility traces, threshold adaptation, refractory dynamics, excitability bias, and learning behavior across diverse neuron families, including Leaky Integrate-and-Fire (LIF), Izhikevich, and conductance-based Hodgkin–Huxley (HH) models. The framework is designed to be modular, biophysically grounded, numerically stable, and suitable for downstream plasticity and task-level learning experiments. Phase 1 — Eligibility Trace Validation I. Single Spike Trace Validation (experiment_trace_single_spike.c) ObjectiveValidate correctness of eligibility trace dynamics in response to a single spike. ScopeLIF, Izhikevich (RS, IB, CH, FS, LTS), Hodgkin–HuxleyEligibility time constants: 5, 10, 20 ms Results Correct impulse response for all neuron families Exponential decay matches analytical solution Baseline-relative error < 1% VerdictFormally validated, invariant, numerically stable II. Superposition Validation (experiment_trace_superposition.c) ObjectiveTest linear superposition under multiple spike events. Results Peak amplitudes within ±1% of theoretical values Accumulation and decay preserved dt = e/100 ensures