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

• URL: http://arxiv.org/abs/2511.03993v1
• Date: Thu, 06 Nov 2025 02:24:51 GMT
• Title: Multiscale Astrocyte Network Calcium Dynamics for Biologically Plausible Intelligence in Anomaly Detection
• Authors: Berk Iskar, Michael Taynnan Barros,
• Abstract summary: Ca$2+$-modulated learning framework draws inspiration from astrocytic Ca$2+$ signaling in the brain.<n>Our approach couples a multicellular astrocyte dynamics simulator with a deep neural network (DNN)
• Score: 0.48700127614643884
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 Ca$^{2+}$-modulated learning framework that draws inspiration from astrocytic Ca$^{2+}$ 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 Ca$^{2+}$ dynamics through three key mechanisms: IP$_3$-mediated Ca$^{2+}$ 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 Ca$^{2+}$-gated model outperforms a matched baseline DNN, achieving up to $\sim$98\% accuracy with reduced false positives and negatives across multiple train/test splits. Importantly, this improved performance comes with negligible runtime overhead once Ca$^{2+}$ trajectories are precomputed. While demonstrated here for cybersecurity applications, this Ca$^{2+}$-modulated learning framework offers a generic solution for streaming detection tasks that require rapid, biologically grounded adaptation to evolving data patterns.

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