Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
从部分测量中恢复稀疏神经连接：一种基于协方差的方法与格兰杰因果性精炼
Massachusetts Institute of Technology
30秒速读
IN SHORT: 通过跨多个实验会话累积协方差统计，实现从部分记录到完整神经连接性的重建。
核心创新
- Methodology Developed a covariance accumulation framework that reconstructs full connectivity matrices from partial observations across multiple recording sessions
- Methodology Introduced Granger-causality refinement via projected gradient descent to enforce biological constraints (sparsity, non-negativity, no autapses)
- Theory Discovered that linear approximation acts as implicit regularization, outperforming oracle estimators with known nonlinearity via Stein-Price identity characterization
主要结论
- 协方差估计器在N=30网络、T=1000时间步和66%测量密度下实现了中位数Frobenius误差0.053（比随机基线提升91%）
- 格兰杰因果性精炼在N=12网络中进一步降低误差6%（从0.100到0.094），同时实现了完美的边检测召回率（中位数=1.0）
- 最佳刺激水平关键取决于测量密度：在100%测量下，零刺激完全失败（误差>4.0），而适度刺激（σ≈0.5）可实现优异恢复（误差∼0.03）
摘要: 从非完整观测中推断神经回路的连接性是神经科学中的一个基本挑战。我们提出了一种基于协方差的方法，用于从多个记录会话中的稀疏、部分测量中估计循环神经网络的权重矩阵。通过在观察到不同神经元子集的会话中累积成对协方差估计，我们重建了完整的连接矩阵，而无需同时记录所有神经元。一个格兰杰因果性精炼步骤通过投影梯度下降强制执行生物学约束。通过对模拟小脑回路的合成网络进行系统实验，我们描述了一个基本的控制-估计权衡：刺激有助于可识别性但会破坏内在动力学，最佳水平取决于测量密度。我们发现“错误”的线性近似起到了隐式正则化的作用——在所有操作机制下都优于已知非线性的oracle估计器——并通过Stein-Price恒等式提供了精确的表征。