Neuroscience and Neural Engineering

The paper designs an open-source closed-loop system for neuro-prosthetic research that can stimulate and measure neuronal activity with high precision and scalability.

This paper develops a model to predict which cortical neurons are activated by electrical stimulation, finding selective activation across cell types, layers, and stimulation parameters.

The paper presents a microfluidic platform for creating functional neuromuscular junctions using human motor neurons and myotubes, validated by rabies virus tracing.

The paper introduces a 3D-printed microelectrode array with ultra-high density and customizable design for electrophysiological recordings.

This paper introduces a template matching method for spike detection in extracellular recordings, showing improved detection of spikes during high firing rates and noisy conditions.

PASER is a MATLAB-based toolbox for automated analysis of neural recordings in magnetic fields, offering denoising, artefact removal, and spike sorting with integrated evaluation of algorithms.

The paper shows that single-trial epidural field potentials can reliably decode spatial information with high accuracy using machine learning.

The paper classifies naturally evoked compound action potentials using spatiotemporal signatures from multi-contact nerve cuff recordings in rats, achieving moderate accuracy and correlation with original firing rates.

The paper introduces a stereotactic system for accurately targeting deep brain structures in awake head-fixed mice, validated through electrophysiological recordings and histological reconstruction.

This study predicts retinal implant users' phosphenes using a model simulating individual retinal ganglion axon pathways, accurately replicating percept shapes.

This paper introduces a 3D-sharpened silicon shuttle for inserting flexible electrode arrays through dura mater into rat brains, enabling less damaging and faster implantation for chronic recordings.

This study shows that photovoltaic stimulation with capacitive-like pulses improves network-mediated activation of retinal ganglion cells by leveraging inhibitory feedback and long voltage pulses.

The paper introduces a computational framework for noninvasive neurostimulation using spatio-temporal interference, achieving higher spatial precision than existing methods.

This paper introduces high-density multi-fiber photometry arrays for studying brain circuit dynamics in behaving animals, demonstrating simultaneous recordings and optogenetic perturbations across multiple brain regions.

The paper introduces a low-destructive, efficient ultra-high frequency brain stimulation method that reduces nerve adaptation using a low frequency rectangular random wave.

This study evaluates multichannel silicon probes for chronic electrophysiological recordings in large animals, finding that probe cross-sectional area and design affect single-unit performance and long-term viability.

This study uses large-scale spiking neural networks to simulate cortical optogenetic stimulation and develops a protocol for translating visual stimuli into light activation patterns for vision restoration.

This paper introduces See-Shells, transparent polymer skulls that enable long-term optical access to the mouse cortex for simultaneous monitoring and perturbation of neural activity across multiple regions.

A robotic inserter threads flexible thin-film polymer electrodes into cortex one at a time with a stiff needle, steering around visible vessels; demonstrated in rat somatosensory cortex.

This paper evaluates Gaussian mixture models for spike sorting, showing improved performance over existing methods in both simulated and real extracellular recordings.

The paper uses high-density extracellular recordings to classify neurons by brain region and identify dendritic backpropagation through waveform analysis.

The paper develops a computational model to study axonal stimulation in epiretinal visual prostheses and finds that activation thresholds vary with stimulus duration and electrode-retina distance.

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

This paper demonstrates the adaptation of Neuropixels probes for chronic recordings in freely moving mice, enabling long-term neural activity tracking and probe reuse.

This study uses a computational model to recommend optimal electrode placement for retinal implants to reduce axonal activation and improve visual perception.

This study investigates how frequency-dependent electrical stimulation affects calcium activity in cortical neurons, revealing spatiotemporal response patterns that depend on charge density and frequency.

This paper introduces a high-density, long-term electrophysiological recording system for freely behaving rats, enabling multi-region neural activity monitoring over extended periods.

This paper presents a computational framework to simulate visual perception with a retinal prosthesis and assesses how learning might improve inferred visual perception.

The paper evaluates local return stimulation to improve selective activation of retinal ganglion cells at cellular resolution in isolated primate retina.

NeuroRoots is a bio-inspired, multi-channel implant that enables long-term stable neural recording in rats, maintaining signal quality for at least 7 weeks.