Data & Analytics

Mean-field theory linking microscale synaptic motifs to macroscale neural population dynamics. Phenomenological framework integrating connectivity, synaptic transmission, plasticity, and heterogeneity.

Mean-field theory bridging microscale synaptic motifs to macroscale heterogeneous neural dynamics. Derives low-rank equations for P-population networks where chain motifs induce correlations in synaptic variability, enabling microscopic fluctuations to influence mesoscopic dynamics. Requires only 2P latent variables. Use when: modeling brain circuits with fine-scale connectivity, deriving mean-field equations for heterogeneous populations, reverse-engineering connectivity from neural recordings.

Spectral analysis of synaptic matrix eigenvalues for stability, transient dynamics, and memory capacity analysis in sparsely connected neural networks

First systematic application of Successor Representations (SRs) from reinforcement learning to natural language. Trains deep residual network on WikiText-103 to predict future word distributions; structured language representations (noun/verb/adjective categories) emerge spontaneously without explicit linguistic supervision. Establishes bridge between RL, linguistics, and cognitive neuroscience. Based on arXiv:2605.24585 (May 2026). Use when studying successor representations in language, eme...

Subcortical shape variations and their associations with cognition across the 8th decade of life. Longitudinal study using neuroimaging and cognitive data from Lothian Birth Cohort 1936. Analyzes heterogeneous morphological trajectories in hippocampus, thalami, globus pallidi, and ventral DC. Uses ANCOVA and mixed linear model analyses to investigate vertex displacement patterns associated with cognitive aging. Use when studying brain morphology changes, subcortical shape analysis, cognitive ...

Early preconfiguration failure detection methodology for repetitive subconcussive (rSC) brain injuries using high-density EEG. Captures millisecond-level cortical dynamics and spatiotemporal features for sports neurology and concussion screening. Activation: subconcussion, EEG, sports neurology, concussion detection, brain injury.

STSBench: A Large-Scale Dataset for Modeling Neuronal Activity in the Dorsal Stream of Primate Visual Cortex - Skill for understanding and applying the methods from arXiv:2607.15631

Structure-Activity in Nonlinear Spiking Networks

Analysis methodology for structural plasticity in neural networks — evaluating growth vs pruning operators, newborn unit integration stability, and time-sensitive optimization dynamics. Covers forward-active backward-starved phenomenon, insertion stability, and continual learning plasticity.

STRAND (Survival Topological Representation ANalysis of Diagrams) treats persistence diagrams as survival data for hypothesis testing, effect sizes, and vectorisation in neuroscience applications.

Stochastic Graph Heat Modelling methodology for brain connectivity estimation. Uses noise-driven heat diffusion on graphs to estimate directed, multivariate, dynamic, model-based connectivity from neurophysiological data. Extends traditional coherence methods with graph-based PDE formulation and regularization. Activation: brain connectivity, graph heat modelling, neurophysiological data, directed connectivity, coherence, graph PDE, effective connectivity

Stimulus symmetries can confound representational similarity analyses — demonstrates how stimulus symmetries in neural network inputs cause functionally-equivalent representations to produce different, drifting RSM geometries. Based on arXiv:2605.21324.

Extended STDP learning rule for simultaneously learning synaptic connection strengths and delays, validated on unsupervised SNN classification tasks with superior performance over delay-free STDP.

STARS (Spike Tail-Aware Relational Synthesis) - plug-and-play method for ANN-to-SNN Data-Free Knowledge Distillation (DFKD). Augments BN-guided synthesis with Relational Consistency Alignment and Tail-Aware Regularization. Achieves up to 4.6% improvement on CIFAR-10 and 6.7% on CIFAR-100. Activation: SNN knowledge distillation, data-free distillation, ANN-to-SNN conversion, tail-aware regularization, relational consistency, spike threshold dynamics, 无数据蒸馏, 跨模态蒸馏.

SpikingMoE — spike-driven Transformer with LGN-inspired Mixture-of-Experts (MoE) for dynamic computation in SNNs

Spiking Transformers Theory - Effective Dimension analysis framework for Spiking Transformers (S-ViT). Provides theoretical bounds on generalization and robustness using VC dimension, Rademacher complexity, and effective dimension metrics. Use when analyzing Spiking Transformer architectures, evaluating SNN generalization bounds, comparing S-ViT with ANN-ViT capacity, or studying temporal coding effects on model complexity. Triggers: spiking transformer, effective dimension, S-ViT, spiking Vi...

SPATE methodology for spiking-phase adaptive temporal encoding in quantum machine learning. Converts real-valued data into leaky integrate-and-fire spike trains and maps spike statistics to quantum rotations with temporal qubits. Use when: quantum ML encoding, spike-driven temporal encoding, quantum feature preparation, temporal qubits, QML pipeline enhancement.

SPATE methodology for quantum machine learning — spiking-phase adaptive temporal encoding. Converts real-valued features into leaky integrate-and-fire spike trains and maps spike statistics to quantum rotations, augmented with temporal qubits via controlled phase operations. Use when: (1) designing QML pipelines for temporal data, (2) encoding time-series/tabular data into quantum feature spaces, (3) comparing spike-based vs angle/amplitude encoding quality, (4) building hybrid quantum neural...

Differential equation analysis of SNN dynamics. Translates discrete spiking models into continuous ODE/PDE formulations for stability analysis, bifurcation study, and dynamical systems characterization. Activation: SNN differential equations, spiking dynamics analysis, ODE neuron model, bifurcation SNN, continuous-time spiking, dynamical systems neuroscience

SpikeProphecy: First large-scale benchmark for causal, autoregressive neural population spike-count forecasting. Introduces population metric decomposition (temporal fidelity, spatial pattern accuracy, magnitude-invariant alignment) on 105 Neuropixels sessions (~89,800 neurons). arXiv:2605.12992

脉冲时序训练和自发强化神经元集群。研究STDP如何形成共享刺激偏好的强耦合神经元集群，自发动力学期间的脉冲相关性主动强化连接。适用于计算神经科学、STDP学习、神经编码研究。触发词：神经元集群、STDP、脉冲时序、神经编码、自发动力学、neuronal assembly、spike timing、STDP、noise correlation。

Spike Agreement Dependent Plasticity (SADP) - biologically inspired learning rule for SNNs using population-level correlation metrics instead of precise spike timing. Activation triggers: spike agreement, synaptic plasticity, SNN learning, bio-inspired learning, population correlation, neuromorphic learning.

Sparse Mamba Decoder (SMD) for quantum error correction — a defect-centric neural decoder using Mamba state-space model that processes only active detection events (k ≪ d²R) achieving O(k) complexity on surface codes. 95-467x faster than Tesseract near-MLD decoder.

信噪比和样本数量调控神经网络表征对齐的方法论。研究神经网络潜在表征的通用性规律，揭示对齐与数据质量和数量的非平凡依赖关系。适用于表征对齐分析、神经网络可解释性、训练优化。触发词：表征对齐、SNR、样本数量、插值阈值、通用表征。