Publications (24)
Analog Sparse Approximation with Applications to Compressed Sensing
Adam S. Charles, Pierre Garrigues, Christopher J. Rozell
Recent research has shown that performance in signal processing tasks can often be significantly improved by using signal models based on sparse representations, where a signal is…
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…
Stable Filtering for Efficient Dimensionality Reduction of Streaming Manifold Data
Nicholas P. Bertrand, Eva Yezerets, Han Lun Yap +2
Many areas in science and engineering now have access to technologies that enable the rapid collection of overwhelming data volumes. While these datasets are vital for understandin…
Data mining the functional architecture of the brain's circuitry
Adam S. Charles
The brain is a highly complex organ consisting of a myriad of subsystems that flexibly interact and adapt over time and context to enable perception, cognition, and behavior. Under…
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…
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…
Behavior-dLDS: A decomposed linear dynamical systems model for neural activity partially constrained by behavior
Eva Yezerets, En Yang, Misha B. Ahrens +1
Brain-wide recordings of large-scale networks of neurons now provide an unprecedented view into how the brain drives behavior. However, brain activity contains both information dir…
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…
Visualizing the PHATE of Neural Networks
Scott Gigante, Adam S. Charles, Smita Krishnaswamy +1
Understanding why and how certain neural networks outperform others is key to guiding future development of network architectures and optimization methods. To this end, we introduc…
Short Term Memory Capacity in Networks via the Restricted Isometry Property
Adam S. Charles, Han Lun Yap, Christopher J. Rozell
Cortical networks are hypothesized to rely on transient network activity to support short term memory (STM). In this paper we study the capacity of randomly connected recurrent lin…
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…
Fast Two-photon Microscopy by Neuroimaging with Oblong Random Acquisition (NORA)
Esther Whang, Skyler Thomas, Ji Yi +1
Advances in neural imaging have enabled neuroscientists to study how large neural populations conspire to produce perception, behavior and cognition. Despite many advances in optic…
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…
Interpreting Deep Learning: The Machine Learning Rorschach Test?
Adam S. Charles
Theoretical understanding of deep learning is one of the most important tasks facing the statistics and machine learning communities. While deep neural networks (DNNs) originated a…
realSEUDO for real-time calcium imaging analysis
Iuliia Dmitrieva, Sergey Babkin, Adam S. Charles
Closed-loop neuroscience experimentation, where recorded neural activity is used to modify the experiment on-the-fly, is critical for deducing causal connections and optimizing exp…
Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation
Adam S. Charles, Christopher J. Rozell
Signal estimation from incomplete observations improves as more signal structure can be exploited in the inference process. Classic algorithms (e.g., Kalman filtering) have exploit…
Bayesian In Vivo Tracking of Synapses using Joint Poisson Deconvolution and Diffeomorphic Registration
Shashwat Kumar, Dominic M. Padova, Binish Narang +6
Synapses are densely packed submicron structures that dynamically reorganize during learning and memory formation. Longitudinal \textit{in vivo} imaging of fluorescently tagged syn…
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…
Data Processing of Functional Optical Microscopy for Neuroscience
Hadas Benisty, Alexander Song, Gal Mishne +1
Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators. To realize the potential of these massive spa…
An Efficient and Flexible Spike Train Model via Empirical Bayes
Qi She, Xiaoli Wu, Beth Jelfs +2
Accurate statistical models of neural spike responses can characterize the information carried by neural populations. But the limited samples of spike counts during recording usual…
Learning spatially-correlated temporal dictionaries for calcium imaging
Gal Mishne, Adam S. Charles
Calcium imaging has become a fundamental neural imaging technique, aiming to recover the individual activity of hundreds of neurons in a cortical region. Current methods (mostly ma…
Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer
Nicholas P. Bertrand, Adam S. Charles, John Lee +2
Tracking algorithms such as the Kalman filter aim to improve inference performance by leveraging the temporal dynamics in streaming observations. However, the tracking regularizers…
Partitioning Neural Co-Variability
Skyler Thomas, Brandon J. Zhu, Kathleen E. Cullen +1
Trial-to-trial variability of neural responses has been linked to important aspects of neural computation and is essential for understanding how neuronal populations respond. While…
Unsupervised discovery of the shared and private geometry in multi-view data
Sai Koukuntla, Joshua B. Julian, Jesse C. Kaminsky +4
Studying complex real-world phenomena often involves data from multiple views (e.g. sensor modalities or brain regions), each capturing different aspects of the underlying system.…