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- Druggen 2 Disease Aware Language Model Drug DiscoveryDrugGen-2: generative model that designs small molecules conditioned on disease ontology and target protein sequences. Fine-tuned GPT-2 with SFT + GRPO. Outperforms baselines on diabetic nephropathy targets with improved binding affinities. Use when working with drug-discovery, disease-aware, language-model.Votes: 0GitHub stars: 3
- Arxiv 2609 01735v1 Circuitsdna Discovering Unconventional Multi Accur**arXiv ID:** 2609.01735v1 **Authors:** Ruichen Qi, Junyi Luo, Xinting Jiang, Quan Cheng, Gregory Kielian, Ben Laurie, Dennis Sylvester, Mehdi Saligane **URL:** http://arxiv.org/abs/2609.01735v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Hardware Aware Vqa AnalysisHardware-aware analysis methodology for Variational Quantum Algorithms (VQAs). Analyzes how hardware compilation (transpilation, qubit mapping, gate decomposition) fundamentally alters expressibility and trainability of parameterized quantum circuits (PQCs). Use when: (1) evaluating VQA performance beyond logical circuit level, (2) analyzing hardware compilation effects on quantum circuit properties, (3) designing PQCs with hardware-aware expressibility/trainability trade-offs, (4) benchmarki...Votes: 0GitHub stars: 3
- Growing Neural Network Breadth Depth TimeDifferentiable cost framework for jointly optimizing neural network breadth, depth, and time - reveals resource trade-offs and human reaction time correlationVotes: 0GitHub stars: 3
- Growing Neural Cellular Automata Gnca Self RepairMethodology for analyzing internal fluctuations in Growing Neural Cellular Automata (GNCA) to understand self-maintenance and self-repair mechanisms. Based on arXiv:2607.12403v1.Votes: 0GitHub stars: 3
- Growing Neural Breadth Depth TimeDifferentiable cost terms for breadth, depth, and time in recurrent convolutional neural networks. Jointly optimizes spatial/temporal resource costs with task errors to produce adaptive computational graphs that grow organically through training.Votes: 0GitHub stars: 3
- Griffiths Phase Brain Criticality脑临界性的 Griffiths 相框架。扩展的临界区域解释个体变异， 平衡鲁棒性与灵活性。结合结构网络模块性和区域异质性。 触发词：脑临界性、Griffiths相、临界点、个体变异、鲁棒性与灵活性、 brain criticality, Griffiths phase, critical point, individual variability。Votes: 0GitHub stars: 3
- Grid Place Cell Co EmergenceFirst unified recurrent network model implementing Dale's Law (every neuron is either excitatory or inhibitory) that trains via masked next-observation prediction to co-emerge both grid and place cells from a single architecture. Use when researching: grid cell emergence, place cell models, entorhinal-hippocampal circuits, spatial navigation neural networks, Dale's Law in computational models, co-emergence of spatial representations, MEC-HPC reciprocal connectivity, developmental spatial cogn...Votes: 0GitHub stars: 3
- Gravitational Wave Lensing Beyond Rays Disordered System ApproacResearch methodology from paper 'Gravitational-wave lensing beyond rays: a disordered-system approach'. arXiv:2604.15313v1. Covers key techniques and approaches for neuroscience research. Activation: gravitational, wave, lensing, astro-ph.COVotes: 0GitHub stars: 3
- Graphene Nanofluidic Memristive DevicesRippled graphene nanopores as fluidic memristive devices with synaptic and neuromorphic functionalities. Bio-inspired ion channel-based computing using nanofluidic memristors. Activation: graphene memristor, fluidic memristive, ion channel computing, nanofluidic synapse.Votes: 0GitHub stars: 3
- Graph Mechanism Quantum PredictionEdge-specific signal propagation on 3D mechanism graphs for quantum yield prediction. Uses graph neural networks to predict fluorescent protein quantum yields from chromophore-region structural graphs.Votes: 0GitHub stars: 3
- Graph Analysis Neuronal Culture Reservoir ComputingGraph analysis of neuronal cultures using Reservoir Computing-derived connectivity maps. Extracts Intrinsic Connectivity Maps (ICM) from neural activity and applies graph centrality measures to quantify network dynamics.Votes: 0GitHub stars: 3
- Graph Analysis Neuronal Culture Connectivity Reservoir ComputingNeuronal culture graph analysis via reservoir computing.Votes: 0GitHub stars: 3
- Gradient Free Snn Evolution StrategiesLow-rank evolution strategies for gradient-free spiking neural network training. EGGROLL method reduces memory from O(mn) to O(r(m+n)) enabling on-chip learning without surrogate gradients. Key benefits: 2.23x speedup, neuromorphic hardware compatibility, no backpropagation infrastructure. Use when: (1) training SNNs on neuromorphic chips, (2) avoiding surrogate gradient approximation, (3) needing gradient-free optimization for discrete spike thresholds, (4) scaling evolution strategies to la...Votes: 0GitHub stars: 3
- Gp Cake Brain Connectivity有效脑连接的因果核建模方法（GP CaKe）。结合积分-微分方程和因果核， 使用高斯过程回归非参数学习，实现因果推断。 触发词：有效连接、因果核、脑连接、高斯过程、GP CaKe、 effective connectivity, causal kernel, Gaussian process, brain connectivity。Votes: 0GitHub stars: 3
- Goxpyriment Go Framework Behavioral Cognitive ExperimentsResearch methodology from paper 'Goxpyriment: A Go Framework for Behavioral and Cognitive Experiments'. arXiv:2604.15245v1. Covers key techniques and approaches for neuroscience research. Activation: goxpyriment, go, framework, q-bio.NCVotes: 0GitHub stars: 3
- Global Workspace Language ModelsMethodology for identifying and interpreting internal mental workspace in language models using Jacobian lens technique, inspired by neuroscience's Global Workspace Theory.Votes: 0GitHub stars: 3
- Global Workspace J Space AnalysisJacobian lens (J-lens) methodology for analyzing language model internal representations using the global workspace framework. Identifies conscious-accessible thoughts in LLMs through J-space patterns.Votes: 0GitHub stars: 3
- Gibbs State AnalysisAnalysis of high-temperature Gibbs states with rapid mixing and external field effects. Studies entanglement structure, computational complexity, and thermalization dynamics. Use when: (1) Analyzing Gibbs states at high temperature, (2) Studying external field effects on quantum entanglement, (3) Investigating rapid mixing Lindbladians, (4) Understanding thermalization crossover scales.Votes: 0GitHub stars: 3
- Geosae Brain Mri SaeGeoSAE methodology for interpretable brain MRI foundation model annotation using geometry-guided sparse autoencoders with age-deconfounded partial correlations. Prevents SAE feature collapse in deep transformer layers, extracts biomarkers from frozen brain MRI foundation models. Achieves MCI-to-AD conversion prediction (AUC 0.746) with 2% embedding dimensions, cross-cohort replication (r=0.97). Use when: GeoSAE, brain MRI foundation model interpretability, sparse autoencoder for medical imagi...Votes: 0GitHub stars: 3
- Geometry Aware Brain Dynamics Mapping V7Enhanced Geometry-Aware Brain Dynamics Mapping using Geometric Basis Functions (GBF) for noninvasive whole-brain spatio-temporal dynamics mapping. Covers basis function construction on brain manifolds, spectral decomposition for multi-scale neural dynamics, and handling of individual anatomical variability. Use when: working with noninvasive brain mapping, fMRI/MEG/EEG source localization, geometric basis functions, brain manifold analysis, whole-brain spatio-temporal modeling, or individual ...Votes: 0GitHub stars: 3
- Geometric Phase Transition Hippocampal MemoryGeometric phase transition methodology for hippocampal memory — extreme spatial memory emerges from a discrete stiffening of hippocampal population geometry from disorganized (mist) to crystalline code. Use when researching: hippocampal memory capacity, neural manifold geometry, topological phase transitions in neural codes, food-caching birds and spatial memory, geometric stability of neural representations, Valiant's Stable Memory Allocator, representational redundancy (geometric tax), exci...Votes: 0GitHub stars: 3
- Geometric Brain Dynamics MappingGeometric Basis Functions (GBF) framework for noninvasive whole-brain spatiotemporal dynamics reconstruction. Uses participant-specific eigenmodes from cortical surface for EEG/MEG source imaging. Trigger words: geometric basis functions, GBF, brain dynamics, source imaging, cortical geometry.Votes: 0GitHub stars: 3
- Geometric Brain Dynamics Mapping V7Geometry-aware framework for noninvasive whole-brain spatiotemporal dynamics mapping using participant-specific Geometric Basis Functions (GBFs). Resolves EEG/MEG inverse problem via cortical-surface eigenmodes. Validated across Meta-Source Benchmark, task-evoked, resting-state, intracranial stimulation, and epilepsy data.Votes: 0GitHub stars: 3