Paper List
-
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...
-
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 ...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
Uncovering statistical structure in large-scale neural activity with Restricted Boltzmann Machines
Université Paris-Saclay, CNRS, INRIA, LISN, Gif-sur-Yvette, France | Universidad Complutense de Madrid, Spain | Princeton University, USA | City University of New York, USA
30秒速读
IN SHORT: This paper addresses the core challenge of modeling large-scale neural population activity (1500-2000 neurons) with interpretable higher-order interactions, overcoming limitations of traditional pairwise maximum-entropy models.
核心创新
- Methodology Demonstrates that Restricted Boltzmann Machines can be trained on thousands of simultaneously recorded neurons using efficient MCMC sampling, achieving accurate reproduction of both pairwise and higher-order correlations.
- Methodology Provides a principled mapping from RBM parameters to explicit multi-body interaction spin models, enabling direct extraction of effective synaptic networks including higher-order couplings.
- Biology Reveals anatomically structured effective interactions: stronger intra-area couplings within visual cortical regions and weaker, more diffuse cross-area couplings, correlating with functional engagement during visual tasks.
主要结论
- RBMs accurately reproduce empirical statistics of neural recordings, matching pairwise correlations, higher-order correlations, and global population activity distributions with high fidelity.
- The inferred effective couplings show clear anatomical organization: intra-visual cortical interactions are stronger and more coherent than cross-area couplings, consistent with functional specialization.
- Despite being trained on static snapshots, RBM-generated samples via MCMC simulations accurately capture global neural relaxation dynamics, suggesting the model encodes temporal structure implicitly.
摘要: Large-scale electrophysiological recordings now allow simultaneous monitoring of thousands of neurons across multiple brain regions, revealing structured variability in neural population activity. Understanding how these collective patterns emerge from microscopic neural interactions requires models that are scalable, predictive, and interpretable. Statistical physics provides principled frameworks to address this complexity, including maximum-entropy models that offer transparent descriptions of collective neural activity in small populations, but remain largely limited to pairwise interactions and modest system sizes. Here, we use Restricted Boltzmann Machines (RBMs) to model the activity of ∼1500–2000 simultaneously recorded neurons from the Allen Institute Visual Behavior Neuropixels dataset, spanning multiple cortical and subcortical regions of the mouse brain. RBMs are energy-based models that extend the maximum-entropy framework through latent variables, enabling the capture of higher-order dependencies while allowing explicit extraction of effective synaptic networks, including interactions beyond pairwise. Recent advances in efficient Markov Chain sampling and training enable accurate learning of these models at this scale. We show that RBMs reproduce the complex statistics of neural recordings with high accuracy. Generated samples match empirical pairwise and higher-order correlations, as well as global statistics such as the distribution of population activity. Beyond accurate data reconstruction, the inferred parameters provide direct access to effective interactions between neurons, revealing dominant coordination patterns in population activity. These couplings exhibit clear anatomical structure: neurons within visual cortical areas form coherent blocks of stronger interactions, consistent with shared engagement during visually driven behavior, whereas cross-area couplings are weaker and more diffuse. Furthermore, despite not being trained to reproduce temporal dependencies, Markov Chain Monte Carlo simulations of the model accurately reproduce the global neural relaxation dynamics. These results establish RBMs as scalable tools to extract interpretable statistical structure from large-scale neural recordings, linking collective neural activity to the organization of brain regions and task-related behavior.