A5022611002- Neuroscience and Neural Engineering
- Neural dynamics and brain function
- Advanced Memory and Neural Computing
- EEG and Brain-Computer Interfaces
- Photoreceptor and optogenetics research
- Cell Image Analysis Techniques
- Neuroscience and Neuropharmacology Research
- Neural Networks and Applications
- Electrochemical Analysis and Applications
- Pluripotent Stem Cells Research
- Scientific Computing and Data Management
- Force Microscopy Techniques and Applications
- Memory and Neural Mechanisms
- 3D Printing in Biomedical Research
- Brain Tumor Detection and Classification
- Neurobiology and Insect Physiology Research
- Image Processing and 3D Reconstruction
- Knowledge Societies in the 21st Century
- Proteoglycans and glycosaminoglycans research
- Circadian rhythm and melatonin
- Research Data Management Practices
- Non-Invasive Vital Sign Monitoring
- Optical Imaging and Spectroscopy Techniques
- Solar and Space Plasma Dynamics
- Neuropeptides and Animal Physiology
Allen Institute
2023-2025
Allen Institute for Neural Dynamics
2023-2025
University of Oslo
2016-2025
ETH Zurich
2019-2024
University of California, San Diego
2018-2024
Catalyst
2024
Allen Institute for Brain Science
2024
Community Catalyst
2023
Institute of Molecular and Clinical Ophthalmology Basel
2023
La Jolla Bioengineering Institute
2018-2020
Human brain organoids replicate much of the cellular diversity and developmental anatomy human brain. However, physiology neuronal circuits within remains under-explored. With high-density CMOS microelectrode arrays shank electrodes, we captured spontaneous extracellular activity from derived induced pluripotent stem cells. We inferred functional connectivity spike timing, revealing a large number weak connections skeleton significantly fewer strong connections. A benzodiazepine increased...
Non-invasive Brain-Computer Interfaces (BCI) have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods combine Electroencephalography (EEG) functional Near-Infrared Spectroscopy (fNIRS) in an asynchronous Sensory Motor rhythm (SMR)-based BCI. We attempted classify 4 different executed movements, namely, Right-Arm—Left-Arm—Right-Hand—Left-Hand tasks. Previous studies the benefit of EEG-fNIRS combination. However, since normally fNIRS...
Spike sorting is a crucial step in electrophysiological studies of neuronal activity. While many spike packages are available, there little consensus about which most accurate under different experimental conditions. SpikeForest an open-source and reproducible software suite that benchmarks the performance automated algorithms across extensive, curated database ground-truth recordings, displaying results interactively on continuously-updating website. With contributions from eleven...
Abstract Recording from a large neuronal population of neurons is crucial challenge to unravel how information processed by the brain. In this review, we highlight recent advances made in field ‘spike sorting’, which arguably very essential processing step extract activity extracellular recordings. More specifically, target challenges faced newly manufactured high-density multi-electrode array devices (HD-MEA), e.g. Neuropixels probes. Among them, cover depth prominent problem drifts...
Abstract A growing consensus that the brain is a mechanosensitive organ driving need for tools mechanically stimulate and simultaneously record electrophysiological response of neurons within neuronal networks. Here we introduce synchronized combination atomic force microscopy, high-density microelectrode array fluorescence microscopy to monitor networks characterize individual at piconewton sensitivity nanometre precision while monitoring their activity subcellular spatial millisecond...
Recent advances in the field of cellular reprogramming have opened a route to studying fundamental mechanisms underlying common neurological disorders. High-density microelectrode-arrays (HD-MEAs) provide unprecedented means study neuronal physiology at different scales, ranging from network through single-neuron subcellular features. In this work, HD-MEAs are used vitro characterize and compare human induced-pluripotent-stem-cell-derived dopaminergic motor neurons, including isogenic lines...
Abstract Perineuronal nets (PNNs) are a condensed form of extracellular matrix primarily found around parvalbumin-expressing (PV+) interneurons. The postnatal maturation PV+ neurons is accompanied with the formation PNNs and reduced plasticity. Alterations in PNN neuron function have been described for mental disorders such as schizophrenia autism. highly dependent on aggrecan, proteoglycan encoded by ACAN gene, but it remains unknown if produced themselves. Thus, we established knockout...
The active K2 dwarf epsilon Eri has been extensively characterized, both as a young solar analog and more recently an exoplanet host star. As one of the nearest brightest stars in sky, it provides unparalleled opportunity to constrain stellar dynamo theory beyond Sun. We confirm document 3 year magnetic activity cycle originally reported by Hatzes coworkers, we examine archival data from previous observations spanning 45 years. show coexisting 13 periods leading into broad minimum that...
Neural circuits typically consist of many different types neurons, and one faces a challenge in disentangling their individual contributions measured neural activity. Classification cells into inhibitory excitatory neurons localization on the basis extracellular recordings are frequently employed procedures. Current approaches, however, need lot human intervention, which makes them slow, biased, unreliable. In light recent advances deep learning techniques exploiting availability neuron...
Abstract When recording neural activity from extracellular electrodes, both in vivo and vitro , spike sorting is a required very important processing step that allows for identification of single neurons’ activity. Spike complex algorithmic procedure, recent years many groups have attempted to tackle this problem, resulting numerous methods software packages. However, validation techniques complicated. It an inherently unsupervised problem it hard find universal metrics evaluate performance....
In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such usually involves use patch-clamp technique to record somatic voltage signals under different experimental conditions. The data then used fit many parameters model. While patching soma is currently gold-standard approach build models, several studies have also evidenced a richness dynamics in dendritic and axonal sections. Recording from alone...
Objective. Mechanistic modeling of neurons is an essential component computational neuroscience that enables scientists to simulate, explain, and explore neural activity. The conventional approach simulation extracellular recordings first computes transmembrane currents using the cable equation then sums their contribution model potential. This two-step relies on assumption space infinite homogeneous conductive medium, while measurements are performed probes. main purpose this paper assess...
High-density neural devices are now offering the possibility to record from neuronal populations in vivo at unprecedented scale. However, mechanical drifts often observed these recordings currently a major issue for "spike sorting," an essential analysis step identify activity of single neurons extracellular signals. Although several strategies have been proposed compensate such drifts, lack proper benchmarks makes it hard assess quality and effectiveness motion correction. In this paper, we...
Abstract The transition from juvenile to adult is accompanied by the maturation of inhibitory parvalbumin-positive (PV+) neurons and reduced plasticity. This involves formation perineuronal nets (PNNs), a dense configuration extracellular matrix that predominantly envelops neurons. Aggrecan, proteoglycan encoded ACAN gene, has been shown have key role in PNNs as knock-out brain reactivates plasticity, but contribution different cell populations unknown Here, we establish characterize mouse...
A major goal in systems neuroscience is to determine the causal relationship between neural activity and behavior. To this end, methods that combine monitoring activity, behavioral tracking, targeted manipulation of neurons closed-loop are powerful tools. However, commercial allow these types experiments usually expensive rely on non-standardized data formats proprietary software which may hinder user-modifications for specific needs. In order promote reproducibility data-sharing science,...
Abstract Much development has been directed towards improving the performance and automation of spike sorting. This continuous development, while essential, contributed to an over-saturation new, incompatible tools that hinders rigorous benchmarking complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed unify preexisting sorting technologies into single codebase facilitate straightforward comparison adoption different...
Abstract Scientific progress depends on reliable and reproducible results. Progress can also be accelerated when data are shared re-analyzed to address new questions. Current approaches storing analyzing neural typically involve bespoke formats software that make replication, as well the subsequent reuse of data, difficult if not impossible. To these challenges, we created Spyglass , an open-source framework enables analyses sharing both intermediate final results within across labs. uses...
High-density multi-electrode arrays (HD-MEAs) have revolutionized electrophysiology by enabling the recording of neuronal activity with unprecedented spatial and temporal resolution. However, analysing these large-scale datasets poses significant challenges, including artefact removal, spike sorting, accurate assessments synchronization. Here, we present two Python-based tools, 'spikeNburst' 'nicespike', designed to address challenges provide a scalable solution for comprehensive analysis...
Electrophysiological recordings capture signals from hundreds of neurons simultaneously, but isolating single-unit activity often requires manual curation due to limitations in spike-sorting algorithms. As dataset sizes keep increasing, the time and expertise required for accurate consistent human pose a major challenge experimental labs. To address this issue, we developed UnitRefine, classification toolbox that leverages diverse machine-learning algorithms minimize efforts. Using acute...
Spatial code of grid cells is independent theta oscillations and phase precession.
Neurons communicate with each other by sending action potentials (APs) through their axons. The velocity of axonal signal propagation describes how fast electrical APs can travel. This be affected in a human brain several pathologies, including multiple sclerosis, traumatic injury and channelopathies. High-density microelectrode arrays (HD-MEAs) provide unprecedented spatio-temporal resolution to extracellularly record neural activity. high density the recording electrodes enables image...
In neural electrophysiology, spike sorting allows to separate different neurons from extracellularly measured recordings. It is an essential processing step in order understand activity and it unsupervised problem nature, since no ground truth information available. There are several available packages, but many of them require a manual intervention curate the results, which makes process time consuming hard reproduce. Here, we focus on high-density Multi-Electrode Array (MEA) recordings...
Objective.With the rapid adoption of high-density electrode arrays for recording neural activity, electrophysiology data volumes within labs and across field are growing at unprecedented rates. For example, a one-hour with 384-channel Neuropixels probe generates over 80 GB raw data. These large carry high cost, especially if researchers plan to store analyze their in cloud. Thus, there is pressing need strategies that can reduce footprint each experiment.Approach.Here, we establish set...
With the latest development in design and fabrication of high-density Multi-Electrode Arrays (MEA) for in-vivo neural recordings, spatiotemporal information recorded signals allows refined estimation a neuron's location around probe. In parallel, advances computational models activity enables simulation recordings from neurons with detailed morphology. Our approach uses deep learning algorithms on large set such data to extract 3D position neuronal somata. Multi-compartment 13 different...