A databank for intracellular electrophysiological mapping of the adult somatosensory cortex

Lantyer, Angelica da Silva; Calcini, Niccolò; Bijlsma, Ate; Kole, Koen; Emmelkamp, Melanie; Peeters, Manon; Scheenen, Wim J.J.M.; Zeldenrust, Fleur; Celikel, Tansu

doi:10.1093/gigascience/giy147

Cited by 14 publications

(25 citation statements)

References 34 publications

(35 reference statements)

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“…For the somatosensory cortical recordings, the experimental procedures and data are found in da Silva Lantyer et al . [ 21 ]. The analyzed cortical data were from current-clamp recordings of pyramidal regular-spiking (RS) neurons (n = 27) and fast-spiking (FS) neurons (n = 7) in layers (L2/3) of the primary somatosensory cortex in adult mice.…”

Section: Methodsmentioning

confidence: 99%

New methods for quantifying rapidity of action potential onset differentiate neuron types

2021

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Two new methods for quantifying the rapidity of action potential onset have lower relative standard deviations and better distinguish neuron cell types than current methods. Action potentials (APs) in most central mammalian neurons exhibit sharp onset dynamics. The main views explaining such an abrupt onset differ. Some studies suggest sharp onsets reflect cooperative sodium channels activation, while others suggest they reflect AP backpropagation from the axon initial segment. However, AP onset rapidity is defined subjectively in these studies, often using the slope at an arbitrary value on the phase plot. Thus, we proposed more systematic methods using the membrane potential’s second-time derivative (V¨m) peak width. Here, the AP rapidity was measured for four different cortical and hippocampal neuron types using four quantification methods: the inverse of full-width at the half maximum of the V¨m peak (IFWd2), the inverse of half-width at the half maximum of the V¨m peak (IHWd2), the phase plot slope, and the error ratio method. The IFWd2 and IHWd2 methods show the smallest variation among neurons of the same type. Furthermore, the AP rapidity, using the V¨m peak width methods, significantly differentiates between different types of neurons, indicating that AP rapidity can be used to classify neuron types. The AP rapidity measured using the IFWd2 method was able to differentiate between all four neuron types analyzed. Therefore, the V¨m peak width methods provide another sensitive tool to investigate the mechanisms impacting the AP onset dynamics.

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Section: Methodsmentioning

confidence: 99%

New methods for quantifying rapidity of action potential onset differentiate neuron types

2021

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“…To that end, we first performed 'classical' step-and-hold current clamp experiments, so that we could classify the cells. Whole-cell recordings were made from pyramidal cells and interneurons in layer 2/3 (L2/3) of mouse barrel cortical slices [16]. Cells were classified as either 'excitatory' or 'inhibitory' based on their electrophysiological responses to a standard current-step protocol (Fig 1, see Materials & Methods).…”

Section: Excitatory Neurons Show Strong Adaptationmentioning

confidence: 99%

“…We measured the information encoding properties of pyramidal cells and interneurons in L2/3 of the mouse barrel cortex. We chose a combination of ex-vivo experiments (da Silva Lantyer et al, 2018) and computational modelling to unravel both the threshold behaviour and the information encoding properties of excitatory and inhibitory neurons, using a recently developed method to estimate the mutual information between input and output in an ex-vivo setup (Zeldenrust et al, 2017). In vivo, excitatory neurons are shown to have a high action potential threshold (Crochet et al, 2011) and high selectivity (Ranjbar-Slamloo and Arabzadeh, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…We measured the information encoding properties of excitatory and inhibitory neurons in L2/3 of the mouse barrel cortex. We chose a combination of ex-vivo experiments [16] and computational modelling to unravel both the threshold behaviour and the information encoding properties of excitatory and inhibitory neurons, using a recently developed method to estimate the mutual information between input and output in an ex-vivo setup [17]. This method has several advantages: instead of the traditionally long (~1 hour) experiments that are needed to obtain a single mutual information estimate [18][19][20][21][22], this method needs only about 5 minutes of recording to obtain an information transfer estimate.…”

Section: Introductionmentioning

confidence: 99%

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The tuning of tuning: how adaptation influences single cell information transfer

Zeldenrust

Calcini

Yan

et al. 2020

Preprint

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Sensory neurons reconstruct the world from action potentials (spikes) impinging on them. Recent work argues that the formation of sensory representations are cell-type specific, as excitatory and inhibitory neurons use complementary information available in spike trains to represent sensory stimuli. Here, by measuring the mutual information between synaptic input and spike trains, we show that inhibitory and excitatory neurons in the barrel cortex transfer information differently: excitatory neurons show strong threshold adaptation and a reduction of intracellular information transfer with increasing firing rates. Inhibitory neurons, on the other hand, show threshold behaviour that facilitates broadband information transfer. We propose that cell-type specific intracellular information transfer is the rate-limiting step for neuronal communication across synaptically coupled networks. Ultimately, at high firing rates, the reduction of information transfer by excitatory neurons and its facilitation by inhibitory neurons together provides a mechanism for sparse coding and information compression in cortical networks.

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“…With its topographical organization, well-characterized structural and functional organization, and its ever growing number of publicly available molecular, cellular and behavioural big datasets (Azarfar et al, 2018b;da Silva Lantyer et al, 2018;Kole et al, 2017Kole et al, , 2018a, the barrel column is ideally suited as a model system for computational reconstruction of circuit organization and function. Accordingly, large-scale computational models of the rodent barrel cortex ranging from detailed reconstructed models that need to be run on a supercomputer (Phoka et al, 2012;Sharp et al, 2014) to much less detailed and computationally expensive models (Bernardi et al, 2020) have been developed.…”

Section: Introductionmentioning

confidence: 99%

Cortical representation of touch in silico

Huang

Zeldenrust

Celikel

2020

Preprint

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With its six layers and ~12000 neurons, a cortical column is a complex network whose function is plausibly greater than the sum of its constituents'. Functional characterization of its network components will require going beyond the brute-force modulation of the neural activity of a small group of neurons. Here we introduce an open-source, biologically inspired, computationally efficient network model of the somatosensory cortex's granular and supragranular layers after reconstructing the barrel cortex in soma resolution. Comparisons of the network activity to empirical observations showed that the in silico network replicates the known properties of touch representations and whisker deprivation-induced changes in synaptic strength induced in vivo. Simulations show that the history of the membrane potential acts as a spatial filter that determines the presynaptic population of neurons contributing to a post-synaptic action potential; this spatial filtering might be critical for synaptic integration of top-down and bottom-up information.

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A databank for intracellular electrophysiological mapping of the adult somatosensory cortex

Cited by 14 publications

References 34 publications

New methods for quantifying rapidity of action potential onset differentiate neuron types

New methods for quantifying rapidity of action potential onset differentiate neuron types

The tuning of tuning: how adaptation influences single cell information transfer

Cortical representation of touch in silico

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