Classification of electrophysiological and morphological neuron types in the mouse visual cortex

Abstract

Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To profile morpho-electric properties of mammalian neurons systematically, we established a single cell characterization pipeline using standardized patch clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly-accessible online database, the Allen Cell Types Database, to display these data sets. Intrinsic physiological and morphological properties were measured from over 1,800 neurons from the adult laboratory mouse visual cortex. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We establish a taxonomy of morphologically-and electrophysiologically-defined cell types for this region of cortex with 17 e-types and 35 m-types, as well as an initial correspondence with previously-defined transcriptomic cell types using the same transgenic mouse lines. INTRODUCTION Neurons of the mammalian neocortex exhibit diverse physiological and morphological characteristics. Classifying these neurons into cell types, following Plato's dictum to "carve

Activity labeling in vivo using CaMPARI2 reveals electrophysiological differences between neurons with high and low firing rate set points

“…Recently, there has been a strong push to catalog and sort all of the neurons in various brain regions, or even the entire brain Gouwens et al, 2019;Phillips et al, 2019;Saunders et al, 2018;Sugino et al, 2019;Tasic et al, 2018;Zeisel et al, 2018Zeisel et al, , 2015, using various approaches to separate cells into classes. Most popular have been techniques that sort cells based on their transcriptional profile or projection targets, though efforts are ongoing to incorporate this anatomical and transcriptional data with electrophysiological data (Gouwens et al, 2019).…”

“…Recently, there has been a strong push to catalog and sort all of the neurons in various brain regions, or even the entire brain Gouwens et al, 2019;Phillips et al, 2019;Saunders et al, 2018;Sugino et al, 2019;Tasic et al, 2018;Zeisel et al, 2018Zeisel et al, , 2015, using various approaches to separate cells into classes. Most popular have been techniques that sort cells based on their transcriptional profile or projection targets, though efforts are ongoing to incorporate this anatomical and transcriptional data with electrophysiological data (Gouwens et al, 2019). Given the characteristic lognormal distribution of firing rates across multiple regions of cortex (Buzsáki and Mizuseki, 2014) and the stability of individual neurons within this distribution (Dhawale et al, 2017;Hengen et al, 2016), we argue that FRSP represents a feature of cellular identity that is orthogonal to anatomical or transcriptional cell type.…”

Efficient and robust coding in heterogeneous recurrent networks

“…The first term on the right of equation (5) shows that neuron m is convolving the input s(t) with an input filter g in m (t) that is a flipped and shifted version of the filter neuron m represents:…”

Morphological pseudotime ordering and fate mapping reveals diversification of cerebellar inhibitory interneurons

“…This approach requires sparse labeling of genetically or lineage-related cell types, which is facilitated by transgenic drivers and viral vectors (Harris et al 2014; He et al 2016). Paired with advancements in statistical and computational methods, neuronal subtypes can be effectively parsed using single neuron anatomy, as illustrated for sensory afferents in the skin (Wu et al 2012), olfactory bulb neurons (Tavakoli et al 2018), and pyramidal and GABAergic interneurons in the cortex ; Kanari et al 2019; Gouwens et al 2019). Drawing fine divisions between subtypes can be challenging however, as heterogeneity and continuous variation within cell types are commonly observed (Cembrowski and Scala et al 2020).…”