Maya-Prana: Metabolic Plasticity Budget for Continual Learning in Affective Spiking Neural Networks

Version 2 (2026-08-04). Revised to the NLL Universal Paper Format v6. The original text is retained in full; nothing has been deleted. Corrections appear as marked blocks placed at the section that carries the claim, and each one states what the paper said, what the data show, the corrected claim, why it happened, and what still stands. This version withdraws the biological-validation claim.Prana reads exactly 1.0000 in every condition including at 3.5× cost, and the code shows why — one quiet batch refills the budget entirely — so the mechanism is a no-op and its non-activation cannot validate anything; depletion and non-depletion were both read as confirming, so no outcome could disconfirm.Condition F — Buddhi gate removed, flat learning rate — is the paper's best result at 13.68% AA and −51.20% BWT, and now leads the findings. Everything below this line is the original description from version 1. It is retained unchanged for the record. Where it conflicts with the corrections above, the corrections stand. Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2. Maya-Prana introduces a metabolic plasticity budget for continual learning in spiking neural networks, modelling the brain's Astrocyte-Neuron Lactate Shuttle (ANLS) as a scalar resource called Prana that governs how much synaptic learning a system can sustain per unit time, depleting under gradient load and recovering during low-activity periods. The core biological finding is one of resilience: calibrated to the ANLS literature, Prana maintains a full budget (1.0000) across all 10 tasks of Split-CIFAR-100, confirming that the astrocytic metabolic supply does not fail under standard cognitive load. A six-condition ablation demonstrates that the canonical system (Condition D) achieves an average accuracy of 12.72%, virtually equivalent to fixed Prana=1.0 (Condition C, 12.02%), while removing the surrounding Antahkarana components (Condition B) collapses performance to 10.33% — establishing that Prana resilience is the primary result and that the full cognitive architecture, not any single module, drives continual learning stability. This paper is the ninth and final entry in the Maya Research Series, completing the computational implementation of the Advaita Vedantic Antahkarana across 13 papers, with the Bhaya Quiescence Law (β* ≤ 0.32%) and Buddhi S-Curve Determinism (R²=1.0000) confirmed throughout the series. Series: Part of the Maya Research Series — 13 papers implementing the Advaita Vedantic Antahkarana as computational primitives in spiking neural networks. Bhaya Quiescence Law (β* ≤ 0.32%) and Buddhi S-Curve Determinism (R²=1.0000) confirmed across all papers. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — Maya-Prana and your research context) | Interactive Dashboard | FAQ | Full Series Index — venky2099.github.io Nexus Learning Labs, Bengaluru · UDYAM-KR-02-0122422 · BHASKAR IN-0526-9452JSORCID: 0000-0002-3315-7907 · VAIRAGYA_DECAY_RATE = 0.002315 — an ORCID-derived provenance mark, not an experimental parameter. Each paper's disclosure block states whether it reached a result in that paper.Canary: MayaNexusVS2026NLL_Bengaluru