13 citations · 21 across the 11 of their papers we have counts for
11 papers
Neighbor Embedding for High-Dimensional Sparse Poisson Data
Noga Mudrik, Adam S. Charles
Across many scientific fields, measurements often represent the number of times an event occurs. For example, a document can be represented by word occurrence counts, neural activi…
Multi-Integration of Labels across Categories for Component Identification (MILCCI)
Noga Mudrik, Yuxi Chen, Gal Mishne +1
Many fields collect large-scale temporal data through repeated measurements (trials), where each trial is labeled with a set of metadata variables spanning several categories. For…
Neural mechanisms of predictive processing: a collaborative community experiment through the OpenScope program
Ido Aizenbud, Nicholas Audette, Ryszard Auksztulewicz +50
This review synthesizes advances in predictive processing within the sensory cortex. Predictive processing theorizes that the brain continuously predicts sensory inputs, refining n…
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics
Yenho Chen, Noga Mudrik, Kyle A. Johnsen +3
Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models…
Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training
Jiancheng Xie, Lou C. Kohler Voinov, Noga Mudrik +2
Recurrent neural networks (RNNs) are a widely used tool for sequential data analysis; however, they are still often seen as black boxes. Visualizing the internal dynamics of RNNs i…
CREIMBO: Cross-Regional Ensemble Interactions in Multi-view Brain Observations
Noga Mudrik, Ryan Ly, Oliver Ruebel +1
Modern recordings of neural activity provide diverse observations of neurons across brain areas, conditions, and subjects; presenting an exciting opportunity to reveal the fundamen…
LINOCS: Lookahead Inference of Networked Operators for Continuous Stability
Noga Mudrik, Eva Yezerets, Yenho Chen +2
Identifying latent interactions within complex systems is key to unlocking deeper insights into their operational dynamics, including how their elements affect each other and contr…
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
Noga Mudrik, Gal Mishne, Adam S. Charles
Time series data across scientific domains are often collected under distinct states (e.g., tasks), wherein latent processes (e.g., biological factors) create complex inter- and in…
Multi-Lingual DALL-E Storytime
Noga Mudrik, Adam S. Charles
While recent advancements in artificial intelligence (AI) language models demonstrate cutting-edge performance when working with English texts, equivalent models do not exist in ot…
Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap
Mengmi Zhang, Elisa Pavarino, Xiao Liu +20
As AI becomes increasingly embedded in daily life, ascertaining whether an agent is human is critical. We systematically benchmark AI's ability to imitate humans in three language…
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
Noga Mudrik, Yenho Chen, Eva Yezerets +2
Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how observed neural activity relates to perception and beha…