Alessio Paolo Buccino

The paper introduces a modular framework for spike sorting that allows individual algorithmic steps to be benchmarked and optimized, improving performance on simulated data and matching real data results.

The paper identifies topographic organization in locus coeruleus norepinephrine neurons, linking their morphology, gene expression, and activity to specific brain regions and learning-related behaviors.

The paper presents scalable spike sorting pipelines and benchmarking tools for large-scale electrophysiology data, showing Kilosort4 outperforms Kilosort2.5 and 7x compression has minimal impact on performance.

UnitRefine automates spike sorting curation using machine learning models trained on expert annotations, achieving human-level performance across species and datasets.

This paper introduces two Python tools, spikeNburst and nicespike, for analyzing HD-MEA data, improving spike sorting and synchronization analysis.

MEDiCINe improves motion correction in neural electrophysiology recordings, leading to more accurate spike sorting.

This paper investigates the role of Aggrecan in PV+ interneurons by creating a conditional knockout model and finds that its absence does not affect electrophysiological properties but alters behavioral strategies and anxiety levels.

Spyglass provides a reproducible and shareable framework for neuroscience research, enabling data reuse and analysis across labs using standardized formats and pipelines.

This paper benchmarks motion correction algorithms for high-density extracellular recordings to evaluate their effectiveness in handling mechanical drifts during spike sorting.

This paper evaluates compression strategies for electrophysiology data, finding that lossy compression can significantly reduce file sizes without affecting spike sorting accuracy.

The paper develops an experimental platform to analyze excitatory and inhibitory synaptic inputs in neurons and finds that spiking coincides with rapid changes in the E/I ratio.

This study benchmarks spike sorting performance in the presence of spike collisions using synthetic data, finding that all methods struggle with detecting synchronous spikes from neurons with similar signals.

This paper introduces a multi-modal fitting framework combining patch-clamp and HD-MEA data to build more accurate multicompartment neuron models.

The paper introduces a method to identify monosynaptic connections using dendritic spine Ca2+ imaging and extracellular spiking recordings, validated with simulations.

This study uses high-density electrode arrays to analyze neural activity in human brain organoids, revealing functional connectivity patterns and responses to benzodiazepine treatment.

This paper introduces an automated method to reconstruct axons and estimate action-potential propagation velocities from high-density microelectrode array recordings.

This study uses HD-MEAs to characterize electrophysiological phenotypes of iPSC-derived neurons and assess drug effects in Parkinson's and ALS models.

SpikeInterface provides a unified framework to run, compare, and benchmark spike sorting algorithms, reducing manual curation and enabling comprehensive benchmarking of automated methods.

MEArec simulates extracellular spiking activity to evaluate spike sorting algorithms by generating customizable ground-truth data with challenging features.

SpikeForest benchmarks spike sorting algorithms using a curated database of electrophysiological recordings with ground truth, providing interactive results to guide optimal sorter selection.

This paper introduces a Bayesian model using variational autoencoder for localizing spike sources in extracellular recordings, showing improved accuracy over heuristic methods.

This paper assesses how neural probe shape and size affect extracellular potentials and introduces a correction method to account for their influence.

The paper presents an open-source system for behavioral tracking, electrophysiology, and closed-loop stimulation using Open Ephys and Bonsai.