Biomimetic Spiking Neural Graph Dynamics: Neuromorphic Memory Consolidation and Neurotransmitter Modulation in Autonomous Cognitive Operating Systems
Abstract
Modern autonomous agents predominantly manage state and memory through static vector embeddings and flat nearest-neighbor (k-NN) retrieval mechanisms. While effective for isolated semantic lookups, this mechanical paradigm suffers from severe cognitive pathologies in long-horizon reasoning tasks: lack of temporal recency decay, inability to perform spontaneous associative recall, absence of affect-driven salience filtering, and catastrophic forgetting under continuous context streams. In this paper, we propose, formalize, implement, and validate a Software-Defined Neuromorphic Architecture powered by Biomimetic Spiking Neural Graph Dynamics for autonomous cognitive operating systems running on commodity computing hardware. Under this paradigm, the agent's knowledge manifold is elevated from a passive vector store to an active Spiking Neural Graph (SNG) where every semantic concept node acts as an integrate-and-fire soma and every relation edge operates as a plastic, weighted synapse. Key Neuro-Computational Contributions: • Event-Driven Leaky Integrate-and-Fire (LIF) Soma: Features continuous exponential voltage decay (tau_m = 25 ms), strict refractory inhibition (t_ref = 15 ms), and thresholded action potential generation (V_th = -55.0 mV), enabling natural temporal fading of obsolete context without continuous polling overhead. • Spreading Associative Wavefronts with Sparse Pruning: Propagates action potentials across multi-hop synaptic topologies in under 3.4 ms via event-driven sparse activation and sub-threshold pruning, eliminating exhaustive global vector dot-product sweeps. • Formal Mathematical Proofs: Rigorously proves Wavefront Energy Dissipation and Finite Convergence (guaranteeing zero runaway epileptic loops) and Asymptotic Synaptic Convergence under Spike-Timing-Dependent Plasticity (STDP). • Quad-Neurotransmitter Modulation Matrix: Implements an endogenous chemical balance vector (Dopamine, Noradrenaline, Serotonin, Acetylcholine) governing salience rewards, urgent threat reflexes, persona stability, and synaptic plasticity via an Ornstein-Uhlenbeck homeostatic equilibrium. • Hippocampal Replay & Dream Consolidation: Simulates biological slow-wave sleep during agent quiescence (IDLE state), replaying episodic ring buffer traces to induce STDP and distill short-term interactions into invariant neocortical rules. Key Empirical Benchmark Results (72-Hour Soak Test): • Sub-4ms Associative Retrieval: Wavefront propagation traverses 22,000 synaptic edges in 3.38 ms on commodity multi-core CPUs without GPU acceleration. • 68.4% Memory Footprint Reduction: Dream consolidation continuously prunes quiescent transient nodes while elevating invariant rules, maintaining stable bounded RAM. • Catastrophic Forgetting Elimination: Knowledge interference rate reduced from 24.8% (baseline vector store) down to 0.4% over a continuous 72-hour autonomous lifecycle. Author: Hai Nguyen • Affiliation: I2FLabs Vietnam ORCID: 0009-0000-1113-2998 • License: Creative Commons Attribution 4.0 International (CC BY 4.0) Document Classification: Pure Academic Research Paper (Zero Commercial Artifacts)