Adam S. Charles

Measuring neuronal input-output operations in vivo with random access projection microscopy

This paper introduces SLAP2, a high-speed microscope for measuring submicron synapses and dendritic voltage in behaving mice, revealing nonlinear input-output amplification in neurons.

Discovering compositional, interpretable structure in social behavior

This paper introduces a method to model social behavior using decomposed linear dynamical systems, showing it captures complex, compositional actions more effectively than existing methods.

GIAnT: a Glutamate Imaging Analysis Toolbox

GIAnT is a toolbox for analyzing glutamate imaging data that corrects motion, identifies active synapses, and improves signal quality.

High Axial Resolution Is Necessary for Quantitative Two-Photon Calcium Imaging of Neuronal Populations

The paper shows that low axial resolution in two-photon calcium imaging corrupts neuronal activity measurements and that no analysis pipeline fully corrects these issues.

Uncovering internal states with a robust shared-state multi-neuron GLM-HMM framework

This paper introduces a robust GLM-HMM framework for analyzing neural data to uncover latent internal states and their relationship to behavior.

Revealing abrupt transitions from goal-directed to habitual behavior

This study demonstrates that the transition from goal-directed to habitual behavior is abrupt, using a novel palatability-driven approach and computational modeling to observe behavioral changes.

CUSP: Complex Spike Sorting from Multi-electrode Array Recordings with U-net Sequence-to-Sequence Prediction

CUSP is a deep learning framework for automated detection of complex spikes in neural recordings, achieving expert-level performance and robustness to recording artifacts.

Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed

The paper introduces Cleo, a simulation testbed for systems neuroscience experiments, integrating optogenetics, electrophysiology, and closed-loop control to improve experimental design and model validation.

Improved inference of latent neural states from calcium imaging data

The paper develops improved models for inferring latent neural states from calcium imaging data by integrating spiking and calcium dynamics directly into latent variable frameworks.

A Space-Time Hidden Markov Model for In Vivo NanoScale Synaptic Plasticity Tracking

The paper introduces SynTrack, a novel algorithm for tracking synaptic dynamics in vivo, achieving high accuracy and scalability in longitudinal synapse tracking.

Precise Extraction of Neural Motifs Reveals Multiscale, Parallel Encoding Schemes in Auditory Cortex

The paper develops a new motif detection algorithm to analyze neural activity in auditory cortex, revealing parallel encoding schemes at single neuron and population levels.

Decomposed Linear Dynamical Systems (dLDS) models reveal context-dependent dynamic connectivity in C. elegans

This paper introduces dLDS models to analyze dynamic neural connectivity in C. elegans, revealing context-dependent changes in neural interactions during task-switching and adaptation.

Continuous partitioning of neuronal variability

This paper introduces a continuous model to partition neural variability into stimulus-driven and stochastic components, finding that gain processes follow an exponentiated power law across visual cortical areas.

SLAy-ing oversplitting errors in high-density electrophysiology spike sorting

SLAy automatically merges oversplit spike units in electrophysiology data, improving efficiency and reproducibility compared to manual methods.

Fast and accessible morphology-free functional fluorescence imaging analysis

The paper improves the GraFT algorithm for functional calcium imaging by enhancing speed and accessibility through efficient solvers, compressive sensing, and a graphical user interface.

Automatic detection of fluorescently labeled synapses in volumetric in vivo imaging data

The paper develops a convolutional neural network to detect and segment fluorescently labeled synapses in vivo, enabling large-scale analysis of synapses in living mice.

Sparse-Coding Variational Auto-Encoders

This paper introduces a sparse-coding variational auto-encoder (SVAE) that improves upon sparse coding by using a probabilistic model with a deep neural network, achieving better performance on natural image data.

Multi-day Neuron Tracking in High Density Electrophysiology Recordings using EMD

This paper introduces a neuron tracking method using non-rigid alignment of spike sorted clusters to identify the same neurons across multiple days in high density electrophysiology recordings, achieving an 84% average recovery rate.

Chronic brain functional ultrasound imaging in freely moving rodents performing cognitive tasks

This study validates chronic functional ultrasound imaging in freely moving rodents during cognitive tasks, showing stable recordings and nonlinear relationships between brain dynamics and task difficulty.

Cross-modality supervised image restoration enables nanoscale tracking of synaptic plasticity in living mice

The paper develops a deep learning image restoration method to enhance in vivo synaptic imaging resolution using ex vivo super-resolution techniques, enabling tracking of synaptic plasticity in living mice.

Detecting and Correcting False Transients in Calcium Imaging

The paper identifies and corrects misattribution errors in calcium imaging by developing a metric, visualization tool, and robust estimator for large datasets.

GraFT: Graph Filtered Temporal Dictionary Learning for Functional Neural Imaging

GraFT isolates fluorescing components in neural imaging by learning time traces with spatial maps as coefficients, using a graph filtering model based on temporal activity rather than spatial proximity.

Sequential and efficient neural-population coding of complex task information

The paper investigates how neural populations encode complex task information efficiently and sequentially in mice, finding that task variables are represented by uncorrelated neural modes and that encoding is multiplexed with sequential dynamics.

Neural Anatomy and Optical Microscopy (NAOMi) Simulation for evaluating calcium imaging methods

The paper introduces NAOMi, a simulation framework for evaluating calcium imaging methods using in silico mouse cortex data, comparing segmentation algorithms and optical designs.

Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield

This paper introduces an algorithm to optimize electrode selection in neural recordings, improving neuron isolation quality and yield compared to existing methods.

Dethroning the Fano Factor: a flexible, model-based approach to partitioning neural variability

The paper introduces a flexible model-based approach to analyze neural variability, showing that many neurons exhibit non-quadratic mean-variance relationships and demonstrating improved adaptive stimulus selection.

Volumetric Two-photon Imaging of Neurons Using Stereoscopy (vTwINS)

This paper introduces vTwINS, a volumetric two-photon calcium imaging method that increases the number of neurons recorded while maintaining high frame-rate in mouse brain regions.