We show that a single Hodgkin–Huxley (HH) neuron with Pyragas-type delayed feedback control (DFC) can store multiple symbols as stable periodic orbits, where the specific orbit is selected by tuning the DFC gain K and time delay $\tau$. Sweeping the $(K,\tau )$ parameter plane at fixed bias current $I_{\mathrm{bias}}$ = 10.0 μA/cm² reveals 207 orbit types across 12 topological categories, with inter-spike interval (ISI) means from 5.9 to 56.9 ms. We establish: (i) a write protocol that reliably locks orbits with 13.9 ms median settling time; (ii) a novel Pattern-Oriented Limit-cycle Decoder (POLD) that reads orbits at 100% accuracy from only five observed ISIs (1200 trials across 12 orbits; Wilson 95% CI: 99.7–100%); (iii) a complete single-symbol write–read–erase (W–R–E) cycle with 100% read accuracy, 92% erase verification, and no decay over hold durations up to 50 s; and (iv) a fully validated 12-symbol memory capacity with a read-discriminable upper bound of 67 symbols (11.2× over rate coding; write viability confirmed only for the conservative 12-symbol subset). Reliable orbit addressing needs delay precision of ±2%, which constitutes a write-precision specification and not a fundamental capacity limit. These findings show that parametric delayed feedback is a viable mechanism for limit-cycle-based information storage in conductance-based spiking neurons. The biological interpretation is analogical, not direct: the ±2% delay-precision requirement exceeds what has been demonstrated for biological autaptic variability, and the orbit-coded memory framing is best understood as a computational proof-of-principle aimed at neuromorphic engineering, not as a claim about biological working memory.

Many engineering design problems are formulated as constrained optimization tasks that are nonlinear and nonconvex, and often treated as black boxes. In such cases, metaheuristic algorithms are attractive because they can search complex design spaces without requiring gradient information. In this work, we propose an Enhanced Henry Gas Solubility Optimization (eHGSO) algorithm, which is an improved version of the physics-inspired HGSO method. The enhanced variant introduces six main contributions: (i) a more diverse, population-wide initialization strategy to cover the design space more thoroughly; (ii) adaptive temperature/pressure control parameters that automatically shift the search from global exploration to local refinement; (iii) an elitist archive with differential perturbation that accelerates exploitation around high-quality candidate designs; (iv) a simple combination of the global HGSO search moves with a lightweight gradient-free local search to refine promising solutions; (v) a constraint-handling mechanism that explicitly prioritizes feasible solutions while still allowing exploration near the constraint boundaries; and (vi) a complexity and ablation analysis that quantifies the impact of each mechanism and confirms that they introduce only modest computational overhead. We evaluate eHGSO on four classical constrained engineering design problems: the stepped cantilever beam, the tension/compression spring, the welded beam, and the three-bar truss. Its performance is compared with seventeen recent metaheuristic optimizers over multiple independent runs. eHGSO achieves the best average objective value on the cantilever, spring, and welded-beam problems and shares the best average result on the three-bar truss. Compared to the second-best method, the mean objective is improved by about $0.84\%$ for the cantilever beam and $0.35\%$ for the welded beam, while the spring and truss results are essentially equivalent at four significant figures. Convergence and robustness analyses show that eHGSO reaches high-quality solutions quickly and consistently. Overall, the proposed eHGSO algorithm appears to be a competitive and practical tool for constrained engineering design problems.

We show that a single Hodgkin–Huxley (HH) neuron with Pyragas-type delayed feedback control (DFC) can store multiple symbols as stable periodic orbits, where the specific orbit is selected by tuning the DFC gain K and time delay τ. Sweeping the (K,τ) parameter plane at fixed bias current Ibias=10.0μA/cm2 reveals 207 orbit types across 12 topological categories, with inter-spike interval (ISI) means from 5.9 to 56.9 ms. We establish: (i) a write protocol that reliably locks orbits with 13.9 ms median settling time; (ii) a novel Pattern-Oriented Limit-cycle Decoder (POLD) that reads orbits at 100% accuracy from only 5 observed ISIs; (iii) a full write–read–erase (W–R–E) cycle with 100% read accuracy, 92% erase verification, and no decay over hold durations up to 50 s; and (iv) a fully validated 12-symbol memory capacity, with a read-discriminable upper bound of 67 symbols (11.2× over rate coding) pending write-viability confirmation for the extended set. Reliable orbit addressing needs delay precision of ±2%, which constitutes a write-precision specification and not a fundamental capacity limit. These findings show that parametric delayed feedback is a viable mechanism for limit-cycle-based information storage in conductance-based spiking neurons. The biological interpretation is analogical, not direct: the ±2% delay-precision requirement exceeds what has been demonstrated for biological autaptic variability, and the orbit-coded memory framing is best understood as a computational proof-of-principle aimed at neuromorphic engineering, not as a claim about biological working memory.

Deep learning-based intrusion detection systems offer world-class accuracy in threat classification. They are also generally not easily explainable to security analysts, which represents a major hurdle in their use in real-world Security Operations Centers (SOCs) where explainability and trust are critical. This operational challenge is tackled with a systems-engineered approach combining the CNN-LSTM architecture with the computationally optimized SHAP and LIME approaches for enabling real-time, interpretable threat detection. Unlike novel mathematical formulations, we concentrate on practical innovations in systems engineering that we believe are required to generate explanations in real-time: quantization of the numbers to INT8, execution of explanation algorithms in parallel, asynchronously, and caching of similar traffic patterns. CNN-LSTM combines the convolutional function to capture spatial dependencies and the recurrent function to capture temporal dynamics of network traffic, and SHAP and LIME capture global and local feature attributions, respectively. One of the major innovations is the parallel execution which brings the latency of explanation down from 117 ms (sequential SHAP + LIME) to 46 ms (parallel, cache-miss) and 39 ms (average with caching) and 46 ms (without caching), which is sufficient for operational “real-time” requirements. The framework is evaluated on CICIDS2017 and NSL-KDD benchmark datasets, and results show that it can achieve 98.7% accuracy with 98.6% F1-score and sub-50 ms explanation latency. The results here show that explainability and operational efficiency can be attained with the same level of accuracy in the detection of abnormal events, through careful systems engineering. This paper presents a systems-engineered framework demonstrating the feasibility of real-time, interpretable IDS for deployment in Security Operations Centers (SOCs) and addresses the challenges of combining high-performance deep learning with operational transparency in cybersecurity.

Machine-learning intrusion detectors that score highly on benchmarks often degrade in deployment, where they meet unseen attack families, evasive adversaries and analysts who need stable explanations. We present TRUST-IDS, which fuses a LightGBM branch hardened by constraint-aware adversarial augmentation with a quantile-calibrated k-nearest-neighbour novelty branch. NSGA-II configures its hyper-parameters, augmentation strength, fusion weight and threshold to maximise macro-F1 and robust detection and to minimise complexity, using a validation split that withholds entire attack families; a knee-point rule selects the deployed configuration. On NSL-KDD (five seeds), TRUST-IDS reached 91.90% accuracy on KDDTest+ and 86.11% on KDDTest-21, versus 79.64% and 61.30% for the best baseline. It detected 88.19% of unseen attack types (baseline 45.65%) and retained 89.0% detection under a black-box evasion attack at ε = 0.05 (baseline 60.7%), at the cost of a 12.59% false-positive rate. On UNSW-NB15, shift-aware selection over-fired because benign test traffic had drifted; with conventional selection, the architecture matched the best baseline accuracy (87.45%) with a six-fold smaller booster and improved robustness over LightGBM at every budget. A label-free novelty-quantile diagnostic distinguishes these two regimes.

The edge-of-chaos hypothesis holds that recurrent systems near criticality maximize memory capacity and expressivity, motivating a natural proposal: replace the non-chaotic linear recurrence of state-space models with a learnable chaotic one. We test this with a gain-tunable chaotic cell whose expansion is set by one calibrated parameter, instrumented with a validated Lyapunov estimator, and measure delay-resolved memory capacity against the lagged mutual information each target demands. Trained end to end, the chaotic recurrence confers no benefit over a gated RNN or its own periodic-regime ablation; a gate dose–response identifies the learned gate as the suppressing mechanism. Frozen under the canonical reservoir-computing protocol, it decisively outperforms a matched linear reservoir, yet a regime-pure control ($n=20$) shows that this is not criticality: an ordered reservoir ($\lambda \approx -0.30$) matches or exceeds the critical one, and across six chaotic reservoir constructions, the ordered arm leads on 21 of 30 paired seeds. Criticality pays only where the target reaches far enough back, on Mackey–Glass and the Santa Fe laser, and delay-resolved measurement explains why: the ordered reservoir retains more at lags 1–3, the critical one retains two to three times more at lags 4–12, and across six targets, the critical margin tracks long-lag demand (Pearson $r=+0.97$), changing sign. We propose a two-factor account: bounded nonlinearity under contraction is necessary, and among nonlinear reservoirs, task-matched memory decides.