High-density single-unit human cortical recordings using the Neuropixels probe DOI Creative Commons
Jason E. Chung, Kristin K. Sellers, Matthew K. Leonard

et al.

Neuron, Journal Year: 2022, Volume and Issue: 110(15), P. 2409 - 2421.e3

Published: June 8, 2022

Language: Английский

Toroidal topology of population activity in grid cells DOI Creative Commons
Richard J. Gardner, Erik Hermansen, Marius Pachitariu

et al.

Nature, Journal Year: 2022, Volume and Issue: 602(7895), P. 123 - 128

Published: Jan. 12, 2022

The medial entorhinal cortex is part of a neural system for mapping the position an individual within physical environment

Language: Английский

Citations

289

Optogenetics for light control of biological systems DOI
Valentina Emiliani, Emilia Entcheva, Rainer Hedrich

et al.

Nature Reviews Methods Primers, Journal Year: 2022, Volume and Issue: 2(1)

Published: July 21, 2022

Language: Английский

Citations

257

Large-scale neural recordings call for new insights to link brain and behavior DOI
Anne E. Urai, Brent Doiron, Andrew M. Leifer

et al.

Nature Neuroscience, Journal Year: 2022, Volume and Issue: 25(1), P. 11 - 19

Published: Jan. 1, 2022

Language: Английский

Citations

255

Representational drift in primary olfactory cortex DOI
Carl E. Schoonover, Sarah Ohashi, Richard Axel

et al.

Nature, Journal Year: 2021, Volume and Issue: 594(7864), P. 541 - 546

Published: June 9, 2021

Language: Английский

Citations

249

Training Spiking Neural Networks Using Lessons From Deep Learning DOI Creative Commons
Jason K. Eshraghian, Max Ward, Emre Neftci

et al.

Proceedings of the IEEE, Journal Year: 2023, Volume and Issue: 111(9), P. 1016 - 1054

Published: Sept. 1, 2023

The brain is the perfect place to look for inspiration develop more efficient neural networks. inner workings of our synapses and neurons provide a glimpse at what future deep learning might like. This article serves as tutorial perspective showing how apply lessons learned from several decades research in learning, gradient descent, backpropagation, neuroscience biologically plausible spiking networks (SNNs). We also explore delicate interplay between encoding data spikes process; challenges solutions applying gradient-based SNNs; subtle link temporal backpropagation spike timing-dependent plasticity; move toward online learning. Some ideas are well accepted commonly used among neuromorphic engineering community, while others presented or justified first time here. A series companion interactive tutorials complementary this using Python package, snnTorch , made available: https://snntorch.readthedocs.io/en/latest/tutorials/index.html.

Language: Английский

Citations

232

Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex DOI
Angelique C. Paulk, Yoav Kfir, Arjun Khanna

et al.

Nature Neuroscience, Journal Year: 2022, Volume and Issue: 25(2), P. 252 - 263

Published: Jan. 31, 2022

Language: Английский

Citations

185

Attractor and integrator networks in the brain DOI

Mikail Khona,

Ila Fiete

Nature reviews. Neuroscience, Journal Year: 2022, Volume and Issue: 23(12), P. 744 - 766

Published: Nov. 3, 2022

Language: Английский

Citations

184

Flexible brain–computer interfaces DOI
Xin Tang, Hao Shen, Siyuan Zhao

et al.

Nature Electronics, Journal Year: 2023, Volume and Issue: 6(2), P. 109 - 118

Published: Feb. 2, 2023

Language: Английский

Citations

177

Solving the spike sorting problem with Kilosort DOI Creative Commons
Marius Pachitariu, Shashwat Sridhar, Carsen Stringer

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 7, 2023

Spike sorting is the computational process of extracting firing times single neurons from recordings local electrical fields. This an important but hard problem in neuroscience, complicated by non-stationarity and dense overlap fields between nearby neurons. To solve spike problem, we have continuously developed over past eight years a framework known as Kilosort. paper describes various algorithmic steps introduced different versions We also report development Kilosort4, new version with substantially improved performance due to clustering algorithms inspired graph-based approaches. test Kilosort, realistic simulation which uses densely sampled real experiments generate non-stationary waveforms noise. find that nearly all Kilosort outperform other on variety simulated conditions, Kilosort4 performs best cases, correctly identifying even low amplitudes small spatial extents high drift conditions.

Language: Английский

Citations

158

A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement DOI Creative Commons
H. Inagaki, Susu Chen, Margreet C. Ridder

et al.

Cell, Journal Year: 2022, Volume and Issue: 185(6), P. 1065 - 1081.e23

Published: March 1, 2022

Motor behaviors are often planned long before execution but only released after specific sensory events. Planning and each associated with distinct patterns of motor cortex activity. Key questions how these dynamic activity generated they relate to behavior. Here, we investigate the multi-regional neural circuits that link an auditory "Go cue" transition from planning directional licking. Ascending glutamatergic neurons in midbrain reticular pedunculopontine nuclei show short latency phasic changes spike rate selective for Go cue. This signal is transmitted via thalamus cortex, where it triggers a rapid reorganization state planning-related command, which turn drives appropriate movement. Our studies can control cortical dynamics precise

Language: Английский

Citations

152