Liam Paninski

Linear NeuroPaint: An Efficient Baseline for Cross-Session Neural Alignment and Inpainting

Linear NeuroPaint evaluates a linear model for cross-session neural alignment and inpainting, finding it less accurate than nonlinear models but computationally efficient.

Age-related changes in behavioural and neural variability in a decision-making task

This study examines age-related changes in neural and behavioural variability during a decision-making task in mice, finding increased neural variability and reduced variability quenching in specific brain regions.

Lightning Pose 3D: an uncertainty-aware framework for data-efficient multi-view animal pose estimation

This paper introduces a framework for multi-view animal pose estimation that improves accuracy and uncertainty estimation with limited labeled data, validated across multiple species.

DENDRO: Recovery and denoising of whole-tree dendritic voltagefrom 2D voltage movies

DENDRO recovers and denoises 3D dendritic voltage from 2D voltage movies using a microscope model and a self-supervised neural network, achieving high accuracy on simulated and real data.

Exploiting correlations across trials and behavioral sessions to improve neural decoding

This paper improves neural decoding by exploiting correlations across trials and sessions, showing better accuracy for four behaviors in mice using interpretable models.

Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing

The paper develops computational tools for rapid monosynaptic connectivity mapping in neural circuits using holographic stimulation and model-based compressed sensing.

Brain-wide representations of prior information in mouse decision-making

Mice infer stimulus-side priors from recent choices; prior signals are decodable pre-stimulus in 24.0% to 30.2% of 242 brain regions, including early visual and motor areas.

Computational optimization of two-photon holographic stimulation sites in vivo

This paper introduces a computational method to reduce off-target stimulation in two-photon holographic optogenetics by optimizing stimulation sites using adaptive non-negative basis function regression.

A common computational and neural anomaly across mouse models of autism

The paper shows that mice with ASD-related mutations exhibit similar computational and neural anomalies in prior updating during decision-making.

Reproducibility of in-vivo electrophysiological measurements in mice

This study evaluates the reproducibility of electrophysiological measurements across ten labs using standardized protocols and finds that variability in electrode targeting and statistical methods affects reproducibility.

DREDge: robust motion correction for high-density extracellular recordings across species

DREDge improves motion correction in high-density extracellular recordings across species, enhancing reliability and reducing errors in electrophysiological data.

Dendritic excitations govern back-propagation via a spike-rate accelerometer

The paper investigates dendritic excitations in CA1 pyramidal neurons, showing how locally generated Na+ spikes drive history-dependent spike back-propagation and influence associative plasticity.

Coordination and persistence of aggressive visual communication in Siamese fighting fish

This study investigates turn-taking aggressive behavior in Siamese fighting fish, identifying a brain region involved in persistence but not coordination of social interactions.

Rarely categorical and highly separable: how neural representations change along the cortical hierarchy

The paper analyzes neural representations across the cortical hierarchy and finds that while categorical encoding is rare within regions, overall cortical structure supports categorical organization and maximal separability.

In vivo cell-type and brain region classification via multimodal contrastive learning

This paper introduces NEMO, a multimodal contrastive learning method for classifying cell types and brain regions from electrophysiological data, achieving state-of-the-art results on two opto-tagged datasets and the International Brain Laboratory Brain-wide Map dataset.

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools

Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization

The paper introduces PhoRC, a method to remove photocurrent artifacts in optogenetic connectivity mapping data using constrained matrix factorization, improving the accuracy of synaptic connection detection.

Identifying Interpretable Latent Factors with Sparse Component Analysis

This paper introduces SCA, an unsupervised method for identifying interpretable latent factors in neural data, and shows its effectiveness across different datasets.

Towards robust and generalizable representations of extracellular data using contrastive learning

This work introduces a contrastive learning framework for extracellular data, showing improved performance over specialized methods in spike sorting and cell-type classification.

Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes

This paper introduces a spike sorting-free decoding method using density-based modeling to improve neural decoding performance without explicitly clustering spikes.

maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data

maskNMF improves demixing of dense calcium imaging data by denoising, sparsening, and using constrained non-negative matrix factorization to accurately recover neural activity.

Flygenvectors: The spatial and temporal structure of neural activity across the fly brain

The paper uses SCAPE microscopy to analyze neural activity in Drosophila brains during different behaviors, revealing both behavior-related and residual high-dimensional activity patterns.

Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings

This paper introduces NP Ultra, a high-density electrode that improves neuronal recording yield, detection, and cell type classification in mice.

DARTsort: A modular drift tracking spike sorter for high-density multi-electrode probes

DARTsort improves spike sorting by incorporating drift tracking, outperforming Kilosort 2.5 on simulated data with drift and noise.

Robust online multiband drift estimation in electrophysiology data

This paper introduces DREDge, an algorithm for robust online drift estimation in electrophysiology data, improving scalability and temporal resolution for human and mouse recordings.

Data architecture for a large-scale neuroscience collaboration

The paper describes a modular data architecture for a neuroscience collaboration, enabling data contribution, access, and analysis across multiple labs.

Bayesian target optimisation for high-precision holographic optogenetics

This paper introduces Bayesian target optimisation to reduce off-target stimulation in optogenetics, showing improved precision in simulations and in vitro experiments.

Neuroscience Cloud Analysis As a Service

This paper introduces a cloud-based platform that provides automated, reproducible, and accessible data analysis tools for neuroscience, improving efficiency and reducing infrastructure barriers.

Versatile Multiple Object Tracking in Sparse 2D/3D Videos Via Diffeomorphic Image Registration

The paper introduces ZephIR, a semi-supervised multiple object tracking framework for 2D/3D videos, demonstrating its versatility in tracking biological systems.

Improved blind demixing methods for recovering dense neuronal morphology from barcode imaging data

This study develops blind demixing methods and a neural network to recover neuronal morphology from barcode imaging data, showing accurate recovery is feasible with sufficient barcode signal density.