CellExplorer: A framework for visualizing and characterizing single neurons

The large diversity of neuron types of the brain, provides the means by which cortical circuits perform complex operations. Neuron types can be described by a broad set of electrophysiological characteristics, afferent inputs, neuron targets and molecular features. To quantify, visualize, and standardize these features, we developed the open-source Matlab-based framework, CellExplorer. It consists of three components: a processing module, a flexible data structure, and a powerful graphical interface. The processing module calculates standardized physiological metrics, performs neuron type classification from electrophysiological features, finds putative monosynaptic connections and saves it to a standardized yet flexible machine-readable format. The graphical interface makes it possible to explore any combination of computed features at the speed of a mouse click. The framework allows users to process, curate and relate their data to a growing publicly available collection of neurons. In addition to data mining, CellExplorer can link genetically identified cell types to physiological properties of tens of thousands of single neurons collected across laboratories, potentially leading to interlaboratory standards of single cell metrics.

[1]  W. Burke,et al.  Single‐unit recording from antidromically activated optic radiation neurones , 1962, The Journal of physiology.

[2]  H B Barlow,et al.  Single units and sensation: a neuron doctrine for perceptual psychology? , 1972, Perception.

[3]  G. Buzsáki,et al.  tFast Network Oscillations in the Hippocampal CA1 Region of the Behaving Rat , 1999, The Journal of Neuroscience.

[4]  J. Csicsvari,et al.  Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. , 2000, Journal of neurophysiology.

[5]  Chris J. McBain,et al.  Interneurons unbound , 2001, Nature Reviews Neuroscience.

[6]  J. Csicsvari,et al.  Massively parallel recording of unit and local field potentials with silicon-based electrodes. , 2003, Journal of neurophysiology.

[7]  R. Quian Quiroga,et al.  Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering , 2004, Neural Computation.

[8]  G. Buzsáki Large-scale recording of neuronal ensembles , 2004, Nature Neuroscience.

[9]  G. Buzsáki,et al.  Characterization of neocortical principal cells and interneurons by network interactions and extracellular features. , 2004, Journal of neurophysiology.

[10]  K. Deisseroth,et al.  Millisecond-timescale, genetically targeted optical control of neural activity , 2005, Nature Neuroscience.

[11]  Jadin C. Jackson,et al.  Quantitative measures of cluster quality for use in extracellular recordings , 2005, Neuroscience.

[12]  Lynn Hazan,et al.  Klusters, NeuroScope, NDManager: A free software suite for neurophysiological data processing and visualization , 2006, Journal of Neuroscience Methods.

[13]  G. Ascoli,et al.  NeuroMorpho.Org: A Central Resource for Neuronal Morphologies , 2007, The Journal of Neuroscience.

[14]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[15]  G. Buzsáki,et al.  Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex , 2008, Nature Neuroscience.

[16]  P. Somogyi,et al.  Neuronal Diversity and Temporal Dynamics: The Unity of Hippocampal Circuit Operations , 2008, Science.

[17]  Sean M Montgomery,et al.  Entrainment of Neocortical Neurons and Gamma Oscillations by the Hippocampal Theta Rhythm , 2008, Neuron.

[18]  Susana Q. Lima,et al.  PINP: A New Method of Tagging Neuronal Populations for Identification during In Vivo Electrophysiological Recording , 2009, PloS one.

[19]  G. Buzsáki,et al.  Theta Oscillations Provide Temporal Windows for Local Circuit Computation in the Entorhinal-Hippocampal Loop , 2009, Neuron.

[20]  Miguel A. L. Nicolelis,et al.  Principles of neural ensemble physiology underlying the operation of brain–machine interfaces , 2009, Nature Reviews Neuroscience.

[21]  G. Buzsáki,et al.  Hippocampal CA1 pyramidal cells form functionally distinct sublayers , 2011, Nature Neuroscience.

[22]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[23]  Richard M. Leahy,et al.  Brainstorm: A User-Friendly Application for MEG/EEG Analysis , 2011, Comput. Intell. Neurosci..

[24]  G. Fishell,et al.  Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons , 2011, Developmental neurobiology.

[25]  Frances S. Chance,et al.  Erratum: Orthogonal micro-organization of orientation and spatial frequency in primate primary visual cortex , 2013, Nature Neuroscience.

[26]  Eran Stark,et al.  Diode probes for spatiotemporal optical control of multiple neurons in freely moving animals. , 2012, Journal of neurophysiology.

[27]  M. Moser,et al.  Optogenetic Dissection of Entorhinal-Hippocampal Functional Connectivity , 2013, Science.

[28]  G. Buzsáki,et al.  Inhibition-Induced Theta Resonance in Cortical Circuits , 2013, Neuron.

[29]  J Anthony Movshon,et al.  Putting big data to good use in neuroscience , 2014, Nature Neuroscience.

[30]  G. Buzsáki,et al.  The log-dynamic brain: how skewed distributions affect network operations , 2014, Nature Reviews Neuroscience.

[31]  G. Fishell,et al.  Interneuron cell types are fit to function , 2014, Nature.

[32]  Christof Koch,et al.  Neurodata Without Borders: Creating a Common Data Format for Neurophysiology , 2015, Neuron.

[33]  Nelson Spruston,et al.  Faculty Opinions recommendation of Brain computation. Selective information routing by ventral hippocampal CA1 projection neurons. , 2015 .

[34]  Stephen V. David,et al.  Cortical Membrane Potential Signature of Optimal States for Sensory Signal Detection , 2015, Neuron.

[35]  G. Buzsáki,et al.  Internally-organized mechanisms of the head direction sense , 2015, Nature Neuroscience.

[36]  G. Buzsáki,et al.  Tools for probing local circuits: high-density silicon probes combined with optogenetics , 2015, Neuron.

[37]  Nicholas A. Steinmetz,et al.  Diverse coupling of neurons to populations in sensory cortex , 2015, Nature.

[38]  György Buzsáki,et al.  Tasks for inhibitory interneurons in intact brain circuits , 2015, Neuropharmacology.

[39]  C. L. Rees,et al.  Hippocampome.org: a knowledge base of neuron types in the rodent hippocampus , 2015, eLife.

[40]  Andres D. Grosmark,et al.  Diversity in neural firing dynamics supports both rigid and learned hippocampal sequences , 2016, Science.

[41]  Pedro Barquinha,et al.  Validating silicon polytrodes with paired juxtacellular recordings: method and dataset , 2016, bioRxiv.

[42]  Kenneth D. Harris,et al.  Fast and accurate spike sorting of high-channel count probes with KiloSort , 2016, NIPS.

[43]  Kristofer E. Bouchard,et al.  High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination , 2016, Neuron.

[44]  Rune W. Berg,et al.  Lognormal firing rate distribution reveals prominent fluctuation–driven regime in spinal motor networks , 2016, bioRxiv.

[45]  Nelson Spruston,et al.  Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons , 2016, eLife.

[46]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[47]  György Buzsáki,et al.  Physiological Properties and Behavioral Correlates of Hippocampal Granule Cells and Mossy Cells , 2017, Neuron.

[48]  Julien Vermot,et al.  Faculty Opinions recommendation of Neural circuits. Labeling of active neural circuits in vivo with designed calcium integrators. , 2017 .

[49]  Jeremy F. Magland,et al.  A Fully Automated Approach to Spike Sorting , 2017, Neuron.

[50]  G. Buzsáki,et al.  Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks , 2017, Neuron.

[51]  Matteo Carandini,et al.  A tool for analyzing electrode tracks from slice histology , 2018, bioRxiv.

[52]  Pierre Yger,et al.  A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo , 2018, eLife.

[53]  Kirstie J. Whitaker,et al.  Raincloud plots: a multi-platform tool for robust data visualization , 2018, PeerJ Prepr..

[54]  Nicholas A. Steinmetz,et al.  Distributed coding of choice, action, and engagement across the mouse brain , 2019, Nature.

[55]  György Buzsáki,et al.  Cooling of Medial Septum Reveals Theta Phase Lag Coordination of Hippocampal Cell Assemblies , 2019, Neuron.

[56]  Keith C. Cheng,et al.  Enhanced and unified anatomical labeling for a common mouse brain atlas , 2019, Nature Communications.

[57]  Richard M. Leahy,et al.  Integrated open-source software for multiscale electrophysiology , 2019, Scientific Data.

[58]  Oliver Rübel,et al.  NWB:N 2.0: An Accessible Data Standard for Neurophysiology , 2019, bioRxiv.

[59]  Christof Koch,et al.  High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification , 2018, bioRxiv.

[60]  György Buzsáki,et al.  Layer-Specific Physiological Features and Interlaminar Interactions in the Primary Visual Cortex of the Mouse , 2019, Neuron.

[61]  Nicholas A. Steinmetz,et al.  Spontaneous behaviors drive multidimensional, brainwide activity , 2019, Science.

[62]  Brian R. Lee,et al.  Classification of electrophysiological and morphological neuron types in the mouse visual cortex , 2019, Nature Neuroscience.

[63]  G. Buzsáki,et al.  The Buzsaki Lab Databank - Public electrophysiological datasets from awake animals , 2020 .

[64]  L. Ng,et al.  The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas , 2020, Cell.

[65]  D. W. Wheeler,et al.  A Method for Estimating the Potential Synaptic Connections Between Axons and Dendrites From 2D Neuronal Images. , 2021, Bio-protocol.

[66]  Yazan N. Billeh,et al.  Survey of spiking in the mouse visual system reveals functional hierarchy , 2021, Nature.

[67]  D. W. Wheeler,et al.  An update to Hippocampome.org by integrating single-cell phenotypes with circuit function in vivo , 2021, PLoS biology.