Data & Analytics

Pathwise approach to metastability for Galves-Löcherbach (GL) stochastic spiking neural network models. Reviews metastability theory from chemistry to probability theory, provides general definition encompassing GL model variants, surveys established metastability results with self-contained proofs, and identifies open problems. arXiv:2607.05652

Zero-shot Quantum Neural Architecture Search methodology for VQA circuit optimization without classical search loop. Use when: (1) designing variational quantum circuits, (2) optimizing quantum architecture without expensive search, (3) reducing classical overhead in VQA, (4) NISQ-era algorithm design, (5) quantum machine learning circuit selection.

Zero-shot imagined speech decoding from MEG via imagined-to-listened cross-condition mapping. Trains models to map imagined MEG responses to listened responses, then decodes using listened-only decoder. Three-stage pipeline: (1) mapping imagined→listened MEG, (2) train contrastive word decoder on listened MEG with multi-embedding evaluation, (3) decode imagined speech via mapping pipeline on held-out subjects. Use when: imagined speech decoding, MEG BCI, cross-condition neural mapping, zero-s...

YANA: Bridging the Neuromorphic Simulation-to-Hardware Gap. Framework for seamless translation of SNN algorithms from simulation to neuromorphic hardware deployment. Activation: YANA, simulation-to-hardware, neuromorphic deployment, SNN hardware gap.

Skill for applying Cluster-based Sequential Feature Selection (CSFS) to improve feature selection in wind and solar power prediction tasks. Use when working with renewable energy prediction datasets that have many environmental variables and need efficient, model-agnostic feature selection.

Wavelet-Enhanced Mixture-of-Experts (WaveMoE) foundation model for time series forecasting. Use when building time series prediction models, incorporating frequency-domain information, or designing MoE architectures for temporal data.

Multi-Stage Warm-Start (MSWS) deep learning framework for Unit Commitment optimization. Combines neural network warm-starting with MILP constraints to accelerate power grid scheduling. Use for unit commitment, power system optimization, energy scheduling, and MILP warm-starting.

Beyond-symmetry structural design patterns for variational quantum machine learning. Use when designing VQML ansatze that go beyond symmetry constraints, selecting parametrizations that balance expressivity and trainability within symmetry-preserving subspaces, or analyzing structural choices in quantum neural network architectures. Covers equivariant VQA design, symmetry-breaking regularization, and structural ansatz selection criteria.

Variational framework for statistical inference on cyclic interactions in directed networks. Directed interactions as edge flows on simplicial complex evolved under energy-minimizing dynamics, yielding low-dimensional cycle space for recurrent organization. Activation: cyclic interaction, harmonic flow, cycle space, simplicial complex, recurrent network, directed graph cycles.

Variational Quantum Algorithms methodology covering CVQE (Cascaded Variational Quantum Eigensolver), certified QNN training via QIBP, and resource-efficient quantum optimization. Use when designing variational quantum circuits, optimizing NISQ-era algorithms, implementing certified quantum machine learning, or applying quantum algorithms to combinatorial optimization problems. Covers VQE variants, quantum interval bound propagation, compact binary encoding for quantum optimization, and divide...

**arXiv ID:** 2401.05680 **Authors:** Shaswata Mitra, Trisha Chakraborty, Subash Neupane, Aritran Piplai, Sudip Mittal **Published:** 2024-01-11T05:56:29Z **Abstract:** In an increasingly interconnected world, where information is the lifeblood of modern society, regular cyber-attacks sabotage the confidentiality, integrity, and availability of digital systems and information. Additionally, cyber-attacks differ depending on the objective and evolve rapidly to disguise defensive systems. Howev...

Systematic RSA comparison showing untrained CNNs match backpropagation-trained CNNs at V1 visual cortex. Untrained random-weights CNN (rho=0.076) exceeds backprop (rho=0.034) at V1/V2 (p<0.001). STDP achieves highest V1 alignment among trained rules (rho=0.064). Four learning rules (BP, FA, PC, STDP) compared against human fMRI from THINGS-fMRI dataset (720 stimuli, 3 subjects).

Systematic RSA comparison showing untrained CNNs match backpropagation at V1 alignment with human fMRI. Evaluates BP, FA, PC, and STDP learning rules against THINGS-fMRI dataset using 720 stimuli across 3 subjects. Use when studying brain-model alignment, comparing learning rules, or analyzing visual cortex representations via Representational Similarity Analysis.

Systematic RSA comparison showing untrained CNNs match backpropagation-trained CNNs at V1 visual cortex. Trigger words: untrained CNN, backpropagation, RSA, V1, representational similarity

Systematic RSA comparison showing untrained CNNs match backpropagation-trained networks at V1 visual cortex, revealing architecture's dominant role over learning rules in neural alignment. Activation triggers: untrained cnn, backpropagation, v1, rsa, representational similarity, learning rules, architecture-driven.

Systematic RSA comparison showing untrained CNNs match backpropagation-trained CNNs in V1 visual cortex alignment. Large-scale fMRI analysis reveals that random feature detectors can capture V1 representational structure. Keywords: untrained CNN, V1 cortex, backpropagation, RSA, representational similarity, visual cortex, fMRI.

Universal Neural Propagator (UNP) methodology for learning time evolution in many-body quantum systems. Transfers across both Hamiltonians and initial states simultaneously. Activation: neural propagator, quantum dynamics simulation, neural operator learning, quantum state evolution, UNP, universal propagator, quantum foundation model, neural quantum dynamics.

Unifying dynamical systems and graph theory to mechanistically understand computation in neural networks. Combines spectral analysis, community detection, and dynamical systems theory to decompose RNN computation into interpretable sub-circuits. Activation: graph theory neural networks, dynamical systems RNN, mechanistic interpretability, spectral analysis RNN, community detection neural computation.

Systematic methodology for compiling molecular ultrastructure into neural dynamics - bridging microscopic brain structure to computational function. Activation: ultrastructure compiler, molecular neural dynamics, connectome to function, structural biology, neural compilation.

Tsodyks-Markram短时程突触可塑性的混沌动力学。研究确定性TM模型中Shilnikov同宿分岔导致混沌行为的路径，揭示网络动力学不可预测性和对初始条件的敏感性。适用于计算神经科学、突触可塑性建模、混沌动力学分析。触发词：短时程突触可塑性、Tsodyks-Markram模型、Shilnikov分岔、混沌动力学、short-term synaptic plasticity、Tsodyks-Markram model、Shilnikov homoclinic bifurcation、chaotic dynamics。

Trustworthy Quantum Machine Learning roadmap covering reliability, robustness, and security in the NISQ era. Addresses QML-specific risks including probabilistic behavior, device noise, and hybrid pipeline vulnerabilities. Activation: trustworthy QML, quantum ML reliability, QML robustness, NISQ era quantum security, quantum ML safety.

Triple Configuration Brain Networks (TCBN) framework using RNNs to model synergistic effects of exogenous stimuli, task demands, and spontaneous activity in brain network reconfiguration. Keywords: brain networks, cognitive flexibility, RNN, task-switching, network dynamics.

Treatment-Conditioned Diffusion framework for forecasting neurodegenerative disease progression via high-fidelity brain state prediction. Conditions generative process on DaTscan images and levodopa equivalent daily dose. Activation: neurodegenerative, disease progression, Parkinson, diffusion, longitudinal neuroimaging, DaTscan, treatment-conditioned.

Transcranial photobiomodulation (tPBM) therapy for insomnia using EEG biomarkers. Prefrontal cortex near-infrared light stimulation targeting prefrontal hypoactivity and hyperarousal model. Pilot study with college students using EEG spectral analysis and functional connectivity to elucidate therapeutic mechanisms. Use when: neuromodulation therapy, insomnia treatment, EEG biomarkers, photobiomodulation, prefrontal hypoactivity, hyperarousal model, non-invasive brain stimulation, sleep disord...