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- Mirage Multimodal Fmri EncodingMIRAGE - Adaptive multimodal gating framework for whole-brain fMRI encoding. Integrates visual, auditory, and linguistic information via native multimodal backbone with layer-wise feature gating. Predicts brain responses to naturalistic audiovisual stimuli across subjects. Use when: (1) Building brain encoding models with multimodal stimuli, (2) Predicting fMRI responses from movies/videos, (3) Integrating visual-auditory-language features for brain prediction, (4) Interpretable modality-spec...Votes: 0GitHub stars: 3
- Mirage Fmri Mental ImageryMIRAGE methodology — robust multi-modal architecture for translating fMRI-to-image models from vision decoding to mental image reconstruction. Uses linear backbone + multi-modal text/image features with diffusion model; achieves SOTA on NSD-Imagery benchmark. Activation: fMRI mental imagery, MIRAGE, brain decoding, image reconstruction, cross-decoding, NSD-ImageryVotes: 0GitHub stars: 3
- Minimal Network Brain Dynamics Mean FieldInteracting branching model of neural network dynamics with hierarchy of analytical mean-field approximations. Characterizes nonequilibrium phase transitions between disorder and ordered phases, exhibits criticality and self-organized dynamics relevant to brain function. Based on arXiv:2512.22093.Votes: 0GitHub stars: 3
- Mine Neural Encoding Mechanistic InterpretabilityMechanistically Interpretable Neural Encoding (MINE) — applying mechanistic interpretability tools (feature attribution, counterfactual editing) to open the black box of voxel-level neural encoding models. Use when: (1) analyzing which image features drive specific voxel responses, (2) generating interpretable descriptions of neural selectivity, (3) performing causal validation of encoding model features, (4) discovering fine-grained functional organization within category-selective brain reg...Votes: 0GitHub stars: 3
- Mine Mechanistically Interpretable Neural EncodingMINE (Mechanistically Interpretable Neural Encoding) — a framework that applies mechanistic interpretability tools from LLMs to vision encoding models, revealing fine-grained functional selectivity at the voxel level in human visual cortex. (arXiv:2605.16468)Votes: 0GitHub stars: 3
- Mimic Mjx Neuromechanical EmulationMIMIC-MJX framework for neuromechanical emulation of animal behavior by learning biomechanically grounded neural control policies from kinematics. Trains neural controllers to actuate biomechanical animal models in physics simulation to reproduce real kinematic trajectories. Use for motor control modeling, behavioral neuroscience, and integrative neuroscience research involving animal behavior simulation.Votes: 0GitHub stars: 3
- Miim Cps Anomaly DetectionJoint latent clustering anomaly detection for multimodal cyber-physical systems (CPS). Models normal behaviour under the MIIM assumption set (Massive, Implicit, Imbalanced Multimodality) with explicit Gaussian-mixture mode clustering in latent space, scored without reconstruction residuals. Includes difficulty-stratified fair evaluation protocol with raw point-wise metrics, trivial-detector splits, and prevalence-matched F1.Votes: 0GitHub stars: 3
- Metastable Neural States Event SegmentationMetastable neural states as computational units of cognition methodology. Synthesizes event segmentation theory with metastable neural activity, revealing spatio-temporally nested hierarchies and predictive model-driven state transitions. Use when: metastable neural states, event segmentation, neural state hierarchy, cognitive state transitions, predictive processing, naturalistic cognition. arXiv: 2605.31473Votes: 0GitHub stars: 3
- Metastable Mind Neural StatesMetastable neural states as fundamental computational units of cognition - integrating Event Segmentation theory with metastability framework (arXiv:2605.31473v1, May 2026).Votes: 0GitHub stars: 3
- Metastable Mind Event SegmentationMetastable Mind framework synthesizing Event Segmentation (ES) and Metastable Neural Activity (MNA) theories. Neural states as fundamental computational units with spatio-temporally nested hierarchy, predictive models, and modular processing boundaries. Activation: metastable, event segmentation, neural states, cognitive segmentation, metastable neural activity, 亚稳态神经状态, 事件分割.Votes: 0GitHub stars: 3
- Metabolic Quantum Limit MegMetabolic quantum limit methodology for magnetoencephalography (MEG) — derives technology-independent bounds on brain imaging information capacity using quantum sensing limits and neural metabolism.Votes: 0GitHub stars: 3
- Metabolic Quantum Limit Meg MagnetoencephalographyMetabolic quantum limit to the information capacity of magnetoencephalography - 代谢量子极限作为MEG信息容量的基本约束Votes: 0GitHub stars: 3
- Mersenne Numbers Doubling Map梅森数与倍角映射的动力学联系研究。通过角度倍角映射动力学框架，无需显式计算M(n)即可求梅森数的因子。提供替代Lucas-Lehmer检验的动力学方法证明大梅森数为合数。适用于大数素性检验、动力系统数论应用。Votes: 0GitHub stars: 3
- Merlin Photonic QmlMerLin discovery engine for photonic and hybrid quantum machine learning. Embeds linear optical circuit simulation into PyTorch/scikit-learn for end-to-end differentiable training of quantum layers. Use when: (1) building hybrid quantum-classical ML models, (2) reproducing photonic QML benchmarks, (3) designing quantum layer architectures, (4) benchmarking QML against classical baselines, (5) hardware-aware quantum ML testing. Activation: merlin, photonic qml, hybrid quantum machine learning,...Votes: 0GitHub stars: 3
- Mental Fatigue Balance ControlMental fatigue-induced balance disturbance analysis using clustering-based heterogeneity classification. Investigating individual differences in balance control response to cognitive fatigue through AX-CPT and PVT performance metrics. Activation: mental fatigue, balance control, AX-CPT, psychomotor vigilance task, cognitive fatigue heterogeneity.Votes: 0GitHub stars: 3
- Memoryvla Temporal Modeling Robotic ManipulationMemoryVLA++ - Temporal modeling framework for VLA models enabling persistent memory for long-horizon robotic manipulation tasksVotes: 0GitHub stars: 3
- Zeroth Order Adaptation Forgetting TheoryRandomized shaping theory explaining why zeroth-order (ZO) adaptation forgets less than first-order methods in continual learning. Activation triggers: zeroth-order adaptation, ZO continual learning, randomized shaping, gradient-free adaptation, catastrophic forgetting, low-query adaptationVotes: 0GitHub stars: 3
- Membrane Potential Alignment Intracortical BciMembrane Potential Alignment (MPA) - Test-time adaptation method for spiking neural networks in intracortical brain-computer interfaces. Realigns pretrained decoders to shifted neural recordings by matching membrane potential distributions via KL divergence - computationally efficient for implantable hardware. Activation: test-time adaptation, intracortical BCI, membrane potential alignment, SNN adaptation, neural signal shift, KL divergence matching, unsupervised adaptation.Votes: 0GitHub stars: 3
- Mechanistic Bridges Receptors Whole Brain DynamicsFramework for receptor-aware whole-brain modeling that bridges molecular/synaptic scales to whole-brain recordings through mean-field reductions, with explicit validity domains and computational trade-offs.Votes: 0GitHub stars: 3
- Measuring Llms Impact N Day ExploitsAnthropic research (Jun 8, 2026) — Measuring how LLMs dramatically accelerate N-day exploit development; Claude Mythos Preview built 8 working Firefox exploits autonomously and 8 Windows kernel privilege escalation chains, collapsing the historically slow patch-diffing bottleneck.Votes: 0GitHub stars: 3
- Mean Field Oscillatory Dynamics Low Rank AdaptationThis paper develops a dynamical mean-field theory for random recurrent networks with low-rank structure and firing-rate-driven adaptation. The theory reveals how adaptation strength drives networks through four distinct dynamical regimes, providing a unified framework for understanding biological oscillations observed during wakefulness, sleep, and anesthesia.Votes: 0GitHub stars: 3
- Mean Field Multi Scale Brain ModelsUse when bridging molecular to brain scales.Votes: 0GitHub stars: 3
- Mean Field Low Rank Adaptation OscillationsDynamical mean-field theory for low-rank recurrent networks with firing-rate adaptation. Identifies four oscillatory regimes and bifurcation mechanisms linking chaos, Hopf bifurcation, and noise-sustained oscillations to biological rhythms (Up-Down states, waxing-and-waning episodes).Votes: 0GitHub stars: 3
- Mean Field Adaptation Oscillations**arXiv**: [2606.30366v1](https://arxiv.org/abs/2606.30366v1) **Authors**: Bowen W. Zheng, Earl K. Miller, Ila R. Fiete (MIT) **Date**: June 29, 2026 **Keywords**: mean-field theory, oscillatory dynamics, adaptation, low-rank networks, chaotic dynamicsVotes: 0GitHub stars: 3