# Multiscale Astrocyte Network Calcium Dynamics for Biologically Plausible Intelligence in Anomaly Detection

arXiv: https://arxiv.org/abs/2511.03993 · HTML: https://mlsanity.com/paper/2511.03993/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling](https://mlsanity.com/paper/2312.07625/) (2023)
- [Characterizing Learning in Spiking Neural Networks with Astrocyte-Like Units](https://mlsanity.com/paper/2503.06798/) (2025)

## Abstract (arXiv)

Network anomaly detection systems encounter several challenges with traditional detectors trained offline. They become susceptible to concept drift and new threats such as zero-day or polymorphic attacks. To address this limitation, we propose a Ca2+-modulated learning framework that draws inspiration from astrocytic Ca2+ signaling in the brain, where rapid, context-sensitive adaptation enables robust information processing. Our approach couples a multicellular astrocyte dynamics simulator with a deep neural network (DNN). The simulator models astrocytic Ca2+ dynamics through three key mechanisms: IP3-mediated Ca2+ release, SERCA pump uptake, and conductance-aware diffusion through gap junctions between cells. Evaluation of our proposed network on CTU-13 (Neris) network traffic data demonstrates the effectiveness of our biologically plausible approach. The Ca2+-gated model outperforms a matched baseline DNN, achieving up to ~98% accuracy with reduced false positives and negatives across multiple train/test splits. Importantly, this improved performance comes with negligible runtime overhead once Ca2+ trajectories are precomputed. While demonstrated here for cybersecurity applications, this Ca2+-modulated learning framework offers a generic solution for streaming detection tasks that require rapid, biologically grounded adaptation to evolving data patterns.