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- Emergent Generalization Representation LearningEmergent generalization by representation learning in artificial neural networks. An explicit information bottleneck forcing an RNN to learn a low-dimensional representation is necessary for rotational and out-of-distribution generalization in time-series prediction. Uses information-theoretic causal emergence to characterize the memorization-to-generalization transition (non-monotonic down-min-up trajectory) and finds analogous dynamics in CA1 hippocampal activity of mice learning an alterna...Votes: 0GitHub stars: 3
- Embodied Neurocomputation FrameworkEmbodied Neurocomputation framework for interfacing biological neural cultures with scaled task-driven validation. Systems-level approach to multi-variable optimization of encoding/decoding between silicon computing and living biology. Demonstrates that biological neural networks (BNNs) can outperform DQN agents in goal-driven navigation when encoding parameters are properly optimized.Votes: 0GitHub stars: 3
- Electronic Bursting NeuronElectronic bursting neuron hardware design using phase-locked loop (PLL) equations. Novel hybrid approach: start from phenomenological equations, adjust for circuit simplicity, then implement. Enables small neural circuit modeling with well-defined mathematical description.Votes: 0GitHub stars: 3
- Effort Cognitive Cost Llm AlignmentInvestigates whether Large Reasoning Model (LRM) chain-of-thought reasoning effort (inference-time compute budget) aligns with human cognitive costs. Finds that cognitive cost alignment between LRMs and humans is a training-time achievement, robust to inference-time perturbations, supporting compiled rather than online accounts of LRM problem-solving.Votes: 0GitHub stars: 3
- Efficient Coding CriticalityTheoretical framework linking efficient coding to criticality in neural populations. Shows that maximizing Fisher information under resource constraints naturally leads to soft modes, diverging correlation lengths, and power-law neural avalanches, unifying statistical and dynamical perspectives of criticality. Also explains sloppiness in neural systems. Use when studying: critical brain hypothesis, neural avalanches, efficient coding theory, Fisher information in neural populations, soft mode...Votes: 0GitHub stars: 3
- Efficient Coding Criticality SloppinessEfficient coding under resource constraints drives neural systems towards criticality and sloppiness. Links Fisher information maximization to power-law distributions and critical brain hypothesis.Votes: 0GitHub stars: 3
- Efficient Clifford T SynthesisEfficient Clifford+T synthesis methodology for small-angle rotations with application to Trotterization - reducing T gate cost from O(log 1/δ) to Õ(θ²/δ) for small angles in fault-tolerant quantum compilation.Votes: 0GitHub stars: 3
- Effective PlasticityNetwork-based framework for quantifying plasticity as system_size/connectivity_ratio. Defines effective plasticity as normalized measure linked to critical regime. Plasticity drives criticality causally. Activation: effective plasticity, plasticity quantification, network plasticity measure, plasticity criticality, system size connectivity, Branchi plasticity.Votes: 0GitHub stars: 3
- Eeg2vision Multimodal Eeg Framework 2d VisualEEG2Vision — Modular end-to-end EEG-to-image reconstruction framework using diffusion models with MLLM-guided boosting. Evaluates performance across EEG resolutions (128/64/32/24 channels). Enables real-time brain-to-image applications with low-density EEG. Use when: EEG visual reconstruction, brain-to-image, diffusion models for EEG, multimodal LLM for neuroscience, low-density EEG decoding. Trigger: EEG to image, brain reconstruction, visual decoding EEG, diffusion EEG, EEG2Vision, 脑电图像重建, ...Votes: 0GitHub stars: 3
- Eeg Visual Attention DecodingEEG-based visual attention decoding from gaze-fixated neural tracking of motion in natural videos. Addresses eccentricity confounds and eye movement artifacts for brain-computer interface research. Activation: EEG attention decoding, visual attention BCI, eccentricity confound, neural tracking.Votes: 0GitHub stars: 3
- Eeg To Text Real World FeasibilityNeuropsychology-inspired EEG-to-Text benchmark (COFETT) that addresses EEG instability and enables teacher-forcing-free evaluation, providing evidence for real-world EEG2Text feasibility. Use when evaluating or building non-invasive brain-to-text decoders, designing EEG benchmarks, or studying EEG instability in neural decoding.Votes: 0GitHub stars: 3
- Eeg Test Time Adaptation BenchmarkNeuroAdapt-Bench: Systematic benchmark for test-time adaptation (TTA) on EEG foundation models under real-world distribution shifts. Evaluates TTA methods across multiple FMs, tasks, and datasets including extreme modality shifts (Ear-EEG). Finds gradient-based TTA degrades, optimization-free methods more stable.Votes: 0GitHub stars: 3
- Eeg Self Initiated Attention ShiftsSubject-specific analysis of self-initiated attention shifts from EEG using interpretable machine learning. Demonstrates reliable within-subject classification of preparatory EEG activity distinguishing self-initiated vs externally instructed attention shifts. Uses SHAP feature attribution to identify spectral-spatial contributions. Applicable to: personalized BCI, asynchronous brain-machine interfaces, attention decoding, EEG-based voluntary intent detection. Activation: self-initiated atten...Votes: 0GitHub stars: 3
- Eeg Scd Cortical Speech TrackingResearch methodology and findings on using EEG cortical tracking strength (CTS) as a neural marker for early-stage cognitive decline. Combines speech encoding models with linguistic feature analysis to detect subjective cognitive decline (SCD). Use when: studying EEG speech processing, cognitive decline biomarkers, neural tracking of naturalistic speech, or linguistic feature encoding in aging populations.Votes: 0GitHub stars: 3
- Eeg Microstate Tokenizer RepresentationUniversal EEG microstate tokenizer for representation learning across downstream tasks. Clusters continuous EEG into discrete microstate tokens that serve as universal building blocks for sleep staging, emotion recognition, seizure detection, and motor imagery. Provides a tokenization-first approach to EEG encoding, distinct from variational embedding or feature extraction methods. Activation: eeg tokenization, microstate tokenizer, universal eeg representation, discrete eeg tokens, eeg repre...Votes: 0GitHub stars: 3
- Eeg Ieeg Bridge BciBridging scalp EEG and intracranial EEG (iEEG) in BCI via pretrained neural models. Maps non-invasive scalp EEG to iEEG-quality representations, enabling high-fidelity BCI without invasive implants. Uses pretrained models to learn the scalp-to-cortical mapping. Activation: EEG iEEG bridge, scalp to intracranial, BCI translation, non-invasive BCI, cortical reconstruction, EEG-to-iEEG, 脑电皮层映射, 无创BCIVotes: 0GitHub stars: 3
- Eeg Foundation Temporal Correlations BlindnessEEG foundation models lose long-range temporal correlations: framework for analyzing spectral-temporal dissociation and cross-population fragility in EEG foundation models. Provides methodology for testing LRTC recovery via DFA exponent and evaluating cross-cohort transfer performance.Votes: 0GitHub stars: 3
- Eeg Foundation Model AdaptersEEG foundation models with domain adaptation using lightweight adapters. Covers pre-trained EEG encoders, task-specific fine-tuning with adapters, cross-dataset generalization, and efficient deployment. Use when working with EEG foundation models, neural signal pre-training, adapter-based fine-tuning, or cross-dataset EEG classification.Votes: 0GitHub stars: 3
- Eeg Foundation Lrp InterpretabilityLayer-wise Relevance Propagation (LRP) methodology for interpreting EEG foundation models. Extends LRP from CNN-based to Transformer-based EEG models, enabling verification and hypothesis discovery. Activation: EEG interpretability, LRP, EEG foundation model, transformer attribution, Clever Hans EEG, post-hoc attribution.Votes: 0GitHub stars: 3
- Eeg Faar Artifact RejectionFast Automatic Artifact Rejection (FAAR) methodology for EEG motor imagery BCIs. Lightweight automated artifact rejection that computes artifact-sensitive features, derives epoch-level Signal Quality Index, adaptively selects rejection thresholds, and automatically rejects contaminated epochs. Reduces inter-subject variability and addresses BCI illiteracy. arXiv: 2605.12408 (eess.SP). Hajhassani, Aristimunha, Graignic, Mellot, Kusch, Delorme, Semah, Caillet.Votes: 0GitHub stars: 3
- Eeg Channel Adaptation BenchmarkSystematic benchmark of channel adaptation methods for EEG foundation models. Compares Conv1d, SSI, source-space decomposition, and Riemannian re-centering across 5 FMs (5M-157M params), 5 tasks, revealing architecture-dependent optimal methods and probe-SFT asymmetry.Votes: 0GitHub stars: 3
- Ecram Short Term PlasticityCross-layer device-circuit-system co-design framework for implementing short-term plasticity (STP) in neuromorphic hardware using non-equilibrium ECRAM dynamics. Transforms volatile ionic dynamics from device artifacts into computational resources. Use when studying: neuromorphic short-term plasticity, ECRAM synaptic devices, temporal information processing in spiking networks, delay-feedback LIF neurons, hardware-software co-design for neuromorphic circuits, or activity-dependent conductance...Votes: 0GitHub stars: 3
- Ecram Short Term Plasticity NeuromorphicCross-layer device-circuit-system co-design framework for leveraging non-equilibrium ECRAM dynamics as computational resources for short-term plasticity in neuromorphic circuits. ECRAM devices naturally exhibit volatile ionic dynamics that produce transient conductance modulation, which can be exploited for STP rather than treated as unwanted variability.Votes: 0GitHub stars: 3
- Eccentricity Constrained Cnn TrainingMethodology for training CNNs with eccentricity-constrained egocentric video data to reveal adaptive information coding that mirrors primate visual system organization, showing differential task-relevance between foveal and peripheral vision.Votes: 0GitHub stars: 3