Search Results for author:
Found 16 papers, 6 papers with code
Active learning of neural population dynamics using two-photon holographic optogenetics
Using neural population responses to photostimulation in mouse motor cortex, we demonstrate the efficacy of a low-rank linear dynamical systems model, and develop an active learning procedure which takes advantage of low-rank structure to determine informative photostimulation patterns.
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
Our ECBMs address both limitations of existing CBMs, providing higher accuracy and richer concept interpretations.
Attention for Causal Relationship Discovery from Biological Neural Dynamics
This paper explores the potential of the transformer models for learning Granger causality in networks with complex nonlinear dynamics at every node, as in neurobiological and biophysical networks.
Learning Time-Invariant Representations for Individual Neurons from Population Dynamics
This suggests that neuronal activity is a combination of its time-invariant identity and the inputs the neuron receives from the rest of the circuit.
LatticeGen: A Cooperative Framework which Hides Generated Text in a Lattice for Privacy-Aware Generation on Cloud
In the current user-server interaction paradigm of prompted generation with large language models (LLM) on cloud, the server fully controls the generation process, which leaves zero options for users who want to keep the generated text to themselves.
X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics
Comprehensive, synapse-resolution imaging of the brain will be crucial for understanding neuronal computations and function.
The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons
In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories.
im2nerf: Image to Neural Radiance Field in the Wild
We take a step towards addressing this shortcoming by introducing a model that encodes the input image into a disentangled object representation that contains a code for object shape, a code for object appearance, and an estimated camera pose from which the object image is captured.
Subgraph Frequency Distribution Estimation using Graph Neural Networks
Small subgraphs (graphlets) are important features to describe fundamental units of a large network.
Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework
In this work, we show how data labeled with semantically continuous attributes can be utilized to conduct a quantitative evaluation of latent-space interpolation algorithms, for variational autoencoders.
Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity
Brain-wide measurements of activity and anatomical connectivity of the $\textit{C. elegans}$ nervous system in principle allow for the development of detailed mechanistic computational models.
HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps
To address this issue, we introduce a new challenging task to generate HD maps.
Learning Guided Electron Microscopy with Active Acquisition
Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection.
Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate
Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas.
A Probe Towards Understanding GAN and VAE Models
This project report compares some known GAN and VAE models proposed prior to 2017.
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics
Pixel-accurate tracking of objects is a key element in many computer vision applications, often solved by iterated individual object tracking or instance segmentation followed by object matching.
