Stigmergic Neuromorphic Collectives:
A Backpropagation-Free Architecture for
Sovereign Edge Defense

Author: Silvio Persic

Affiliation: Arden University, London

Prepared For: Dr. Varuna De Silva

Abstract

As artificial intelligence deployment scales across critical national infrastructure, the reliance on cloud-tethered, von Neumann architectures utilizing global backpropagation presents a critical vulnerability in both energy consumption and data sovereignty. This paper proposes a novel, biomimetic computing architecture: the Stigmergic Neuromorphic Collective. By deploying an array of edge-based Spiking Neural Networks (SNNs) that utilize localized, unsupervised plasticity (Active Inference) rather than backpropagation, the system remains computationally dormant until an anomaly is detected.

A central aggregator node governs swarm consensus, mathematically evaluating localized weight adjustments and broadcasting the optimized synaptic state globally. This framework establishes an autonomous, zero-trust "collective mind" capable of real-time threat adaptation without exposing raw payload data, serving as a blueprint for sovereign AI deployments.

1. Introduction and The Neuromorphic Imperative

The standard paradigm of deep learning relies on continuously differentiable functions and the chain rule of calculus to perform backpropagation. While effective in controlled, supervised environments, this requires a complete, frozen mathematical map of network states to trace errors backward across centralized hardware. For localized edge defense, where systems must adapt in real-time to novel adversarial inputs without cloud latency or data exfiltration risks, backpropagation is computationally and strategically prohibitive.

To achieve absolute data sovereignty, we must transition from monolithic processing to decentralized, event-driven hardware. This paper outlines an architecture where multiple localized SNNs (the Edge Nodes) monitor highly specific environments. These nodes operate asynchronously, utilizing Difference Predictive Coding (DiffPC) to maintain an internal model of reality, generating computational spikes strictly in the presence of prediction errors (anomalies).

2. Localized Plasticity and Active Inference

In the proposed collective, individual SNN nodes do not wait for a global error calculation. Instead, learning occurs continuously and locally at the silicon level, guided by the Free Energy Principle.

Each node acts as an autonomous agent minimizing Variational Free Energy, representing the mathematical boundary of "surprise" when raw incoming data \(x\) violates the internal prediction \(z\). This minimization is expressed as:

\[ F \approx \mathbb{E}_{q}[\log q(z) - \log p(x, z)] \]

During standard operational flow, \(F\) remains near zero. The silicon circuits mimic a resting biological state, consuming negligible power. When a novel anomaly is introduced, the system incurs a massive prediction error. The local SNN immediately updates its internal synaptic weights \(W_i\) via localized plasticity (such as Spike-Timing-Dependent Plasticity or Forward-Forward state contrasting) to assimilate the new environmental physics.

Crucially, the raw data payload of the anomaly is never retained or transmitted. The node only stores the resulting topological shift in its mathematical weight array.

3. Swarm Consensus and The Aggregator Node

To prevent isolated knowledge silos, the architecture employs a central aggregator node, analogous to a biological neuromodulator broadcast system. The aggregator does not calculate backpropagation or assess raw data; it strictly performs mathematical consensus on the synaptic states of the edge nodes.

When a localized node successfully adapts to an anomaly, it transmits its adjusted weight matrix \(W_i\) alongside a computed confidence scalar \(\alpha_i\), derived from the magnitude of Free Energy minimized during the event. The aggregator executes a Swarm Consensus algorithm to calculate the new global truth:

\[ W_{\text{global}} = \sum_{i=1}^{n} \alpha_i W_i \]

If the localized adaptation is mathematically robust, \(\alpha_i\) is heavily weighted. The aggregator immediately broadcasts \(W_{\text{global}}\) back across the entire neuromorphic collective. Through this stigmergic coordination, nodes that have never encountered the specific anomaly are instantly immunized, effectively rewriting the baseline reality of the entire swarm.

4. Proposed Implementation: Sovereign Sentinel and Iron Vestige

The immediate strategic application of this architecture is within hardened, offline cybersecurity environments. The proposed testbed implementation, designated Project Iron Vestige, deploys this SNN swarm to monitor localized, high-value API endpoints (e.g., hardened FastAPI hosts).

In this environment, edge SNNs ingest raw network traffic telemetry. By strictly analyzing the temporal spikes of the data flow, the nodes establish a baseline of operational "truth." When an offensive maneuver, such as an adversarial payload injection or an abnormal handshake sequence, hits a single node, the local SNN rapidly adjusts its circuits to isolate the anomaly. The subsequent broadcast of \(W_{\text{global}}\) ensures that every other API endpoint across the architecture is immediately fortified against the zero-day exploit, achieving a fully sovereign, self-healing network defense.

5. Strategic Implications for UK AI Infrastructure

For implementations at the level of parliamentary or national defense infrastructure, reliance on hyper-parameterized, cloud-dependent architectures is an unsustainable security vector. The Stigmergic Neuromorphic Collective demonstrates that high-order machine intelligence does not require massive centralized data lakes.

By combining the low-power physical efficiency of silicon SNNs with the mathematical consensus of swarm intelligence, we can deploy autonomous, localized sentinels that learn dynamically, protect data natively, and evolve collectively.