A databank for intracellular electrophysiological mapping of the adult somatosensory cortex

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

doi:10.1101/472043

Cited by 8 publications

(10 citation statements)

References 35 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combined with the systematic molecular, structural and functional reconstruction (see e.g. (Meyer et al, 2010(Meyer et al, , 2013Narayanan et al, 2015;Huang et al, 2016;Kole et al, 2017bKole et al, , 2017aKole et al, , 2018da Silva Lantyer et al, 2018;Kole and Celikel, 2019)) of the these circuits, a comprehensive multi-scale computational map of sensorimotor computation is evolving.…”

Section: Discussionmentioning

confidence: 99%

Development of adaptive motor control for tactile navigation

Azarfar¹,

Celikel²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Navigation is a result of complex sensorimotor computation which requires integration of sensory information in allocentric and egocentric coordinates as the brain computes a motor plan to drive navigation. In this active sensing process, motor commands are adaptively regulated based on prior sensory information. In the darkness, rodents commonly rely on their tactile senses, in particular to their whiskers, to gather the necessary sensory information and instruct navigation. Previous research has shown that rodents can process whisker input to guide mobility even prior to whisking onset by the end of the second postnatal week, however, when and how adaptive sensorimotor control of whisker position matures is still not known. Here, we addressed this question in rats longitudinally as animals searched for a stationary target in darkness. The results showed that juvenile rats perform object localization by controlling their body, but not whisker position, based on the expected location of the target. Adaptive, closed-loop, control of whisker position matures only after the third postnatal week. Computational modeling of the active whisking showed the emergence of the closed-loop control of whisker position and reactive retraction, i.e. whisker retraction that ensures the constancy of duration of tactile sampling, facilitate the maturation of sensorimotor exploration strategies during active sensing. These results argue that adaptive motor control of body and whiskers develop sequentially, and sensorimotor control of whisker position emerges later in postnatal development upon the maturation of intracortical sensorimotor circuits. database to study the principles of active sensing in freely navigating rodents. Gigascience 7, 2018b. Behrens TEJ, Muller TH, Whittington JCR, Mark S, Baram AB, Stachenfeld KL, Kurth-Nelson Z. What is a cognitive map? organizing knowledge for flexible behavior. Neuron 100: 490-509, 2018. Bender KJ, Rangel J, Feldman DE. Development of columnar topography in the excitatory layer 4 to layer 2/3 projection in rat barrel cortex. J Neurosci 23: 8759-8770, 2003. Berg RW, Kleinfeld D. Rhythmic whisking by rat: retraction as well as protraction of the vibrissae is under active muscular control. J Neurophysiol 89: 104-117, 2003. Carvell GE, Simons DJ. Biometric analyses of vibrissal tactile discrimination in the rat. J Neurosci 10: 2638-2648, 1990. Carvell GE, Simons DJ. Task-and subject-related differences in sensorimotor behavior during active touch. Somatosens Mot Res 12: 1-9, 1995. Celikel T, Marx V, Freudenberg F, Zivkovic A, Resnik E, Hasan MT, Licznerski P, Osten P, Rozov A, Seeburg PH, Schwarz MK. Select overexpression of homer1a in dorsal hippocampus impairs spatial working memory. Front Neurosci 1: 97-110, 2007. Celikel T, Sakmann B. Sensory integration across space and in time for decision making in the somatosensory system of rodents. A. The death receptor CD95 activates adult neural stem cells for working memory formation and brain repair. Cell Stem Cell 5: 178-190, 2009. Cramer NP, K...

show abstract

Section: Discussionmentioning

confidence: 99%

Development of adaptive motor control for tactile navigation

Azarfar¹,

Celikel²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The massive size of the subsequent raw voltage traces will make it infeasible to store them for offline processing. Moreover, real-time spike sorting allows for experimental conditions that adapt according to the neural responses that are observed which makes room for wider scientific investigation including but not limited to the neural basis of adaptive sensorimotor computation [78,79], contextual information processing [80][81][82][83] and storage [84][85][86][87], navigation [88][89][90], and information transfer in neural circuits [46,48]. Brain-machine-interfaces (BMI), like limb prosthetics, also necessitate that spike sorting is performed in real-time on a time-scale of hundreds of milliseconds [65], as these are usually controlled by direct neuronal signalling that is measured invasively by an array of electrodes [91].…”

Section: Discussionmentioning

confidence: 99%

PASER for automated analysis of neural signals recorded in pulsating magnetic fields

Brouns

Celikel

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Thanks to the advancements in multichannel intracranial neural recordings, magnetic neuroimaging and magnetic neurostimulation techniques (including magnetogenetics), it is now possible to perform large-scale high-throughput neural recordings while imaging or controlling neural activity in a magnetic field. Analysis of neural recordings performed in a switching magnetic field, however, is not a trivial task as gradient and pulse artefacts interfere with the unit isolation. Here we introduce a toolbox called PASER, Processing and Analysis Schemes for Extracellular Recordings, that performs automated denoising, artefact removal, quality control of electrical recordings, unit classification and visualization. PASER is written in MATLAB and modular by design. The current version integrates with third party applications to provide additional functionality, including data import, spike sorting and the analysis of local field potentials. After the description of the toolbox, we evaluate 9 different spike sorting algorithms based on computational cost, unit yield, unit quality and clustering reliability across varying conditions including self-blurring and noise-reversal. Implementation of the best performing spike sorting algorithm (KiloSort) in the default version of the PASER provides the end user with a fully automated pipeline for quantitative analysis of broadband extracellular signals.PASER can be integrated with any established pipeline that sample neural activity with intracranial electrodes. Unlike the existing algorithmic solutions, PASER provides an end-to-end solution for neural recordings made in switching magnetic fields independent from the number of electrodes and the duration of recordings, thus enables high-throughput analysis of neural activity in a wide range of electro-magnetic recording conditions.

show abstract

“…Electrophysiological recordings were obtained from two databases. Recordings from the somatosensory cortex were obtained from the GigaScience database [ 19 ], whereas recordings from hippocampal neurons were obtained from the CRCNS database [ 20 ]. For the somatosensory cortical recordings, the experimental procedures and data are found in da Silva Lantyer et al .…”

Section: Methodsmentioning

confidence: 99%

“…These recordings were obtained and uploaded to the database by Angelica da Silva Lantyer (AL) and were found to be the lowest noise recordings in that database. The recording labels in the database are given in da Silva Lantyer et al Supporting Information [ 19 ]. For the hippocampal neurons, the experimental procedures and recordings are found in Lee et al ., [ 20 , 22 ].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

2021

View full text Add to dashboard Cite

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.

show abstract

A databank for intracellular electrophysiological mapping of the adult somatosensory cortex

Cited by 8 publications

References 35 publications

Development of adaptive motor control for tactile navigation

Development of adaptive motor control for tactile navigation

PASER for automated analysis of neural signals recorded in pulsating magnetic fields

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

Contact Info

Product

Resources

About