scholarly journals Brain-State-Dependent Modulation of Neuronal Firing and Membrane Potential Dynamics in the Somatosensory Thalamus during Natural Sleep

Cell Reports ◽  
2019 ◽  
Vol 26 (6) ◽  
pp. 1443-1457.e5 ◽  
Author(s):  
Nadia Urbain ◽  
Nicolas Fourcaud-Trocmé ◽  
Samuel Laheux ◽  
Paul A. Salin ◽  
Luc J. Gentet
Keyword(s):  
Membrane Potential ◽  
Neuronal Firing ◽  
Brain State ◽  
Natural Sleep ◽  
Somatosensory Thalamus ◽  
State Dependent

scholarly journals Whisking-Related Changes in Neuronal Firing and Membrane Potential Dynamics in the Somatosensory Thalamus of Awake Mice

Cell Reports ◽  
2015 ◽  
Vol 13 (4) ◽  
pp. 647-656 ◽  
Author(s):  
Nadia Urbain ◽  
Paul A. Salin ◽  
Paul-Antoine Libourel ◽  
Jean-Christophe Comte ◽  
Luc J. Gentet ◽  
...  
Keyword(s):  
Membrane Potential ◽  
Neuronal Firing ◽  
Somatosensory Thalamus

Measurement of spontaneous neuronal membrane activity by time lapse confocal microscopy

1995 ◽  
Vol 53 ◽  
pp. 972-973
Author(s):  
R H. Selinfreund ◽  
A. H. Cornell-Bell
Keyword(s):  
Membrane Potential ◽  
Confocal Microscopy ◽  
Patch Clamp ◽  
Electrical Activity ◽  
Time Lapse ◽  
Neuronal Firing ◽  
Neuronal Membrane ◽  
Pituitary Cells ◽  
Patch Clamp Recording ◽  
Spontaneous Electrical Activity

Cellular electrophysiological properties are normally monitored by standard patch clamp techniques . The combination of membrane potential dyes with time-lapse laser confocal microscopy provides a more direct, least destructive rapid method for monitoring changes in neuronal electrical activity. Using membrane potential dyes we found that spontaneous action potential firing can be detected using time-lapse confocal microscopy. Initially, patch clamp recording techniques were used to verify spontaneous electrical activity in GH4\C1 pituitary cells. It was found that serum depleted cells had reduced spontaneous electrical activity. Brief exposure to the serum derived growth factor, IGF-1, reconstituted electrical activity. We have examined the possibility of developing a rapid fluorescent assay to measure neuronal activity using membrane potential dyes. This neuronal regeneration assay has been adapted to run on a confocal microscope. Quantitative fluorescence is then used to measure a compounds ability to regenerate neuronal firing.The membrane potential dye di-8-ANEPPS was selected for these experiments. Di-8- ANEPPS is internalized slowly, has a high signal to noise ratio (40:1), has a linear fluorescent response to change in voltage.

scholarly journals Brain state-dependent stimulation boosts functional recovery following stroke

2018 ◽  
Vol 85 (1) ◽  
pp. 84-95 ◽  
Author(s):  
Natalie Mrachacz-Kersting ◽  
Andrew J. T. Stevenson ◽  
Helle R. M. Jørgensen ◽  
Kåre Eg Severinsen ◽  
Susan Aliakbaryhosseinabadi ◽  
...  
Keyword(s):  
Functional Recovery ◽  
Brain State ◽  
State Dependent

scholarly journals Sub-optimal Discontinuous Current-Clamp switching rates lead to deceptive mouse neuronal firing

2020 ◽  
Author(s):  
Marin Manuel
Keyword(s):  
Membrane Potential ◽  
Neuronal Firing ◽  
Intracellular Recordings ◽  
Optimal Switching ◽  
Switching Rate ◽  
Current Clamp ◽  
Care Needs ◽  
Cell Potential ◽  
Cell Excitability

AbstractIntracellular recordings using sharp microelectrodes often rely on a technique called Discontinuous Current-Clamp to accurately record the membrane potential while injecting current through the same microelectrode. It is well known that a poor choice of DCC switching rate can lead to under-or over-estimation of the cell potential, however, its effect on the cell firing is rarely discussed. Here, we show that sub-optimal switching rates lead to an overestimation of cell excitability. We performed intracellular recordings of mouse spinal motoneurons and recorded their firing in response to pulses and ramps of current in Bridge and DCC mode at various switching rates. We demonstrate that using an incorrect (too low) DCC frequency leads not only to an underestimation of the input resistance, but also, paradoxically, to an artificial overestimation of the firing of these cells: neurons fire at lower current, and at higher frequencies than at higher DCC rates, or than the same neuron recorded in Bridge mode. These effects are dependent on the membrane time constant of the recorded cell, and special care needs to be taken in large cells with very short time constants. Our work highlights the importance of choosing an appropriate DCC switching rate to obtain not only accurate membrane potential readings but also an accurate representation of the firing of the cell.Significance StatementDiscontinuous Current-Clamp is a technique often used during intracellular recordings in vivo. However, incorrect usage of this technique can lead to incorrect interpretations. Poor choice of the DCC switching rate can lead to under- or over-estimation of the cell potential. In addition, we show here that sub-optimal switching rates lead to an overestimation of the cell excitability.

scholarly journals Brain-State-Dependent Non-Invasive Brain Stimulation and Functional Priming: A Hypothesis

2014 ◽  
Vol 8 ◽  
Author(s):  
Elena G. Sergeeva ◽  
Petra Henrich-Noack ◽  
MichaÅ‚ Bola ◽  
Bernhard A. Sabel
Keyword(s):  
Brain Stimulation ◽  
Brain State ◽  
Non Invasive ◽  
State Dependent

Introduction

1998 ◽  
Author(s):  
Christof Koch
Keyword(s):  
Membrane Potential ◽  
Nerve Cells ◽  
Neuronal Firing ◽  
Western World ◽  
Long Term Memory ◽  
Sensory Data ◽  
Relevant Variables ◽  
Elementary Circuit ◽  
Circuit Designer ◽  
The Brain

The brain computes! This is accepted as a truism by the majority of neuroscientists engaged in discovering the principles employed in the design and operation of nervous systems. What is meant here is that any brain takes the incoming sensory data, encodes them into various biophysical variables, such as the membrane potential or neuronal firing rates, and subsequently performs a very large number of ill-specified operations, frequently termed computations, on these variables to extract relevant features from the input. The outcome of some of these computations can be stored for later access and will, ultimately, control the motor output of the animal in appropriate ways. The present book is dedicated to understanding in detail the biophysical mechanisms responsible for these computations. Its scope is the type of information processing underlying perception and motor control, occurring at the millisecond to fraction of a second time scale. When you look at a pair of stereo images trying to fuse them into a binocular percept, your brain is busily computing away trying to find the “best” solution. What are the computational primitives at the neuronal and subneuronal levels underlying this impressive performance, unmatched by any machine? Naively put and using the language of the electronic circuit designer, the book asks: “What are the diodes and the transistors of the brain?” and “What sort of operations do these elementary circuit elements implement?” Contrary to received opinion, nerve cells are considerably more complex than suggested by work in the neural network community. Like morons, they are reduced to computing nothing but a thresholded sum of their inputs. We know, for instance, that individual nerve cells in the locust perform an operation akin to a multiplication. Given synapses, ionic channels, and membranes, how is this actually carried out? How do neurons integrate, delay, or change their output gain? What are the relevant variables that carry information? The membrane potential? The concentration of intracellular Ca2+ ions? What is their temporal resolution? And how large is the variability of these signals that determines how accurately they can encode information? And what variables are used to store the intermediate results of these computations? And where does long-term memory reside? Natural philosophers and scientists in the western world have always compared the brain to the most advanced technology of the day.

The biochemical basis of relapse and drug response in schizophrenia: review and hypothesis

1991 ◽  
Vol 21 (4) ◽  
pp. 881-895 ◽  
Author(s):  
Daniel P. Van Kammen
Keyword(s):  
Drug Response ◽  
Negative Symptoms ◽  
Antipsychotic Drugs ◽  
Signal To Noise Ratio ◽  
Neuronal Firing ◽  
Variable Response ◽  
Biochemical Basis ◽  
State Dependent ◽  
Show Evidence ◽  
Drug Free

SYNOPSISThis review of the literature suggests that antipsychotic drug response is determined by dopamine (DA) turnover and norepinephrine (NE) activity prior to treatment. The data suggest that NE modulates the DA system. Drug-free psychotic patients with relatively increased DA and NE activity, including release, are more likely to be treatment responsive, while patients who show evidence of enhanced DA and NE activity during treatment with antipsychotic drugs are likely to relapse soon after neuroleptic withdrawal. Basal release of DA and NE is decreased and associated with residual positive and negative symptoms. Improvement during neuroleptic treatment is associated with decreases in DA and NE phasic or stimulus induced release. The variable response to antipsychotic drugs is most likely to be a result of dysregulated DA and NE release, i.e. under state-dependent control, rather than evidence of a heterogeneous aetiology. Because catecholamines regulate gain, signal-to-noise ratio and gating in the brain, this model allows for environmental factors to interact with biochemical state and drug treatment. The author proposes that impaired homeostasis of NE and DA in schizophrenia causes instability in NE and DA neuronal firing and release, presumably related to mechanisms down-stream from the receptors, such as G proteins. This instability of catecholamine release may explain the observed variability in clinical states and drug response in schizophrenia.

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