scholarly journals Subthreshold Membrane-Potential Resonances Shape Spike-Train Patterns in the Entorhinal Cortex

2008 ◽  
Vol 100 (3) ◽  
pp. 1576-1589 ◽  
Author(s):  
T. A. Engel ◽  
L. Schimansky-Geier ◽  
A.V.M. Herz ◽  
S. Schreiber ◽  
I. Erchova
Keyword(s):  
Membrane Potential ◽  
Entorhinal Cortex ◽  
Rhythmic Activity ◽  
Cell Types ◽  
Neuronal Firing ◽  
Type Model ◽  
Fundamental Properties ◽  
Subthreshold Oscillations ◽  
Subthreshold Resonance ◽  
Specific Discharge

Many neurons exhibit subthreshold membrane-potential resonances, such that the largest voltage responses occur at preferred stimulation frequencies. Because subthreshold resonances are known to influence the rhythmic activity at the network level, it is vital to understand how they affect spike generation on the single-cell level. We therefore investigated both resonant and nonresonant neurons of rat entorhinal cortex. A minimal resonate-and-fire type model based on measured physiological parameters captures fundamental properties of neuronal firing statistics surprisingly well and helps to shed light on the mechanisms that shape spike patterns: 1) subthreshold resonance together with a spike-induced reset of subthreshold oscillations leads to spike clustering and 2) spike-induced dynamics influence the fine structure of interspike interval (ISI) distributions and are responsible for ISI correlations appearing at higher firing rates (≥3 Hz). Both mechanisms are likely to account for the specific discharge characteristics of various cell types.

Ionic mechanisms for the subthreshold oscillations and differential electroresponsiveness of medial entorhinal cortex layer II neurons

1993 ◽  
Vol 70 (1) ◽  
pp. 144-157 ◽  
Author(s):  
R. Klink ◽  
A. Alonso
Keyword(s):  
Membrane Potential ◽  
Entorhinal Cortex ◽  
Inward Rectification ◽  
Voltage Level ◽  
Medial Entorhinal Cortex ◽  
Potential Oscillations ◽  
Subthreshold Oscillations ◽  
Outward Rectification ◽  
Ionic Mechanisms ◽  
Membrane Potential Oscillations

1. Layer II of the medial entorhinal cortex is composed of two electrophysiologically and morphologically distinct types of projection neurons: stellate cells (SCs), which are distinguished by rhythmic subthreshold oscillatory activity, and non-SCs. The ionic mechanisms underlying their differential electroresponsiveness, particularly in the subthreshold range of membrane potentials, were investigated in an "in vitro" slice preparation. 2. In both SCs and non-SCs, the apparent membrane input resistance was markedly voltage dependent, respectively decreasing or increasing at hyperpolarized or subthreshold depolarized potential levels. Thus the neurons displayed inward rectification in the hyperpolarizing and depolarizing range. 3. In the depolarizing range, inward rectification was blocked by tetrodotoxin (TTX, 1 microM) in both types of neurons and thus shown to depend on the presence of a persistent low-threshold Na+ conductance (gNap). However, in the presence of TTX, pronounced outward rectification became manifest in the subthreshold depolarizing range of membrane potentials (positive to -60 mV) in the SCs but not in the non-SCs. 4. The rhythmic subthreshold membrane potential oscillations that were present only in the SCs were abolished by TTX and not by Ca2+ conductance block with Cd2+ or Co2+. Subthreshold oscillations thus rely on the activation of voltage-gated Na+, and not Ca2+, conductances. The Ca2+ conductance block also had no effect on the subthreshold outward rectification. 5. Prominent time-dependent inward rectification in the hyperpolarizing range in the SCs persisted after Na(+)- and Ca2+ conductance block. This rectification was not affected by Ba2+ (1 mM), but was blocked by Cs+ (1-4 mM). Therefore, it is most probably generated by a hyperpolarization-activated cationic current (Q-like current). However, the Q-like current appears to play no major role in the generation of subthreshold rhythmic membrane potential oscillations, because these persisted in the presence of Cs+. 6. On the other hand, in the SCs, the fast, sustained, outward rectification that strongly developed (after Na+ conductance block) at the oscillatory voltage level was not affected by Cs+ but was blocked by Ba2+ (1 mM). Barium was also effective in blocking the subthreshold membrane potential oscillations. 7. In the non-SCs, which do not generate subthreshold rhythmic membrane potential oscillations or manifest subthreshold outward rectification in TTX, Ca2+ conductance block abolished spike repolarization and caused the development of long-lasting Na(+)-dependent plateau potentials at a high suprathreshold voltage level. At this level, where prominent delayed rectification is present, the Na+ plateaus sustained rhythmic membrane potential oscillations.(ABSTRACT TRUNCATED AT 400 WORDS)

Differential electroresponsiveness of stellate and pyramidal-like cells of medial entorhinal cortex layer II

1993 ◽  
Vol 70 (1) ◽  
pp. 128-143 ◽  
Author(s):  
A. Alonso ◽  
R. Klink
Keyword(s):  
Membrane Potential ◽  
Entorhinal Cortex ◽  
Morphological Characteristics ◽  
Oscillatory Activity ◽  
Inward Rectification ◽  
Projection Neurons ◽  
Medial Entorhinal Cortex ◽  
Mean Frequency ◽  
Subthreshold Oscillations

1. The electroresponsive properties of neurons from layer II of the rat medial entorhinal cortex (MEC) were studied by intracellular recording under current clamp in an in vitro brain slice preparation. From a total of 184 cells that fulfilled our criteria for recording stability, two groups of projection neurons were distinguished on the basis of their intrinsic biophysical properties and morphological characteristics (demonstrated by intracellular biocytin injection; n = 34). 2. Stellate cells (SCs) were the most abundant (69%). They were highly electroresponsive, and minimal changes (1-3 mV) of membrane potential generated an active response. Subthreshold depolarizing or hyperpolarizing current pulse injection always caused the membrane potential to attain an early peak and then sag to a lower level. Depolarization-induced "sags" were larger and determined early firing in all cells. The voltage-current relationship of SCs was markedly non-linear, demonstrating robust inward rectification in the hyperpolarizing and depolarizing range. 3. SCs generated persistent rhythmic subthreshold voltage oscillations on DC depolarization positive to -60 mV. The mean frequency of the oscillations was 8.6 Hz (theta range) at a membrane potential of approximately -55 mV, at which level occasional single spiking also occurred. At slightly more positive potentials, a striking 1- to 3-Hz repetitive bursting pattern emerged. This consisted of nonadapting trains of spikes ("clusters") interspersed with subthreshold oscillations that had a mean frequency of 21.7 Hz (beta range). 4. Nonstellate cells (39%; mostly pyramidal-like) displayed time-dependent inward rectification that was less pronounced than that of SCs, and minimal depolarization-induced sags. On threshold depolarization, firing was always preceded by a slowly rising ramp depolarization and thus occurred with a long delay. Inward rectification in the depolarizing range was very pronounced. However, non-SCs did not generate persistent rhythmic subthreshold oscillatory activity or spike clusters. 5. Of the electrophysiological parameters quantified, spike threshold, spike duration, depolarizing afterpotential amplitude and apparent membrane time constant demonstrated statistically significant differences between SCs and non-SCs. 6. The repetitive hiring properties in response to square current pulses of short duration (< 500 ms) were also different between SCs and non-SCs. First, most SCs displayed a bilinear frequency-current (f-I) relationship for only the first interspike interval, whereas most non-SCs displayed a bilinear relationship for all intervals. Second, SCs had a much steeper primary f-I slope for early intervals than non-SCs. Finally, SCs displayed more pronounced and faster spike frequency adaptation than non-SCs.(ABSTRACT TRUNCATED AT 400 WORDS)

Frequency Selectivity of Layer II Stellate Cells in the Medial Entorhinal Cortex

2002 ◽  
Vol 88 (5) ◽  
pp. 2422-2429 ◽  
Author(s):  
Julie S. Haas ◽  
John A. White
Keyword(s):  
Entorhinal Cortex ◽  
Stellate Cells ◽  
Mechanistic Explanation ◽  
Input Power ◽  
Total Power ◽  
Frequency Content ◽  
Medial Entorhinal Cortex ◽  
Subthreshold Oscillations ◽  
Subthreshold Resonance

Electrophysiologically, stellate cells (SCs) from layer II of the medial entorhinal cortex (MEC) are distinguished by intrinsic 4- to 12-Hz subthreshold oscillations. These oscillations are thought to impose a pattern of slow periodic firing that may contribute to the parahippocampal theta rhythm in vivo. Using stimuli with systematically differing frequency content, we examined supra- and subthreshold responses in SCs with the goal of understanding how their distinctive characteristics shape these responses. In reaction to repeated presentations of identical, pseudo-random stimuli, the reliability (repeatability) of the spiking response in SCs depends critically on the frequency content of the stimulus. Reliability is optimal for stimuli with a greater proportion of power in the 4- to 12-Hz range. The simplest mechanistic explanation of these results is that rhythmogenic subthreshold membrane mechanisms resonate with inputs containing significant power in the 4- to 12-Hz band, leading to larger subthreshold excursions and thus enhanced reliability. However, close examination of responses rules out this explanation: SCs do show clear subthreshold resonance (i.e., selective amplification of inputs with particular frequency content) in response to sinusoidal stimuli, while simultaneously showing a lack of subthreshold resonance in response to the pseudo-random stimuli used in reliability experiments. Our results support a model with distinctive input-output relationships under subthreshold and suprathreshold conditions. For suprathreshold stimuli, SC spiking seems to best reflect the amount of input power in the theta (4–12 Hz) frequency band. For subthreshold stimuli, we hypothesize that the magnitude of subthreshold theta-range oscillations in SCs reflects the total power, across all frequencies, of the input.

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 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.

Subthreshold oscillations of the membrane potential: a functional synchronizing and timing device

1993 ◽  
Vol 70 (5) ◽  
pp. 2181-2186 ◽  
Author(s):  
I. Lampl ◽  
Y. Yarom
Keyword(s):  
Membrane Potential ◽  
Logic Gate ◽  
Maximum Amplitude ◽  
Sine Wave ◽  
Inferior Olivary Nucleus ◽  
Synaptic Potential ◽  
Synaptic Potentials ◽  
Subthreshold Oscillations ◽  
Inferior Olivary ◽  
Olivary Nucleus

1. Subthreshold membrane potential oscillations have been observed in different types of CNS neurons. In this in vitro study, we examined the possible role of these oscillations by analyzing the responses of neurons from the inferior olivary nucleus to a combined stimulation of sine wave and synaptic potentials. 2. A nonlinear summation of the sine wave and the synaptic potential occurred in olivary neurons; a superlinear summation occurred when the synaptic potential was elicited at the trough of the sine wave or during the rising phase. On the other hand, a less than linear summation occurred when the synaptic potentials were evoked during the falling phase of the wave. 3. Significant changes in the delay of the synaptic responses were observed. As a result of these changes, the maximum amplitude of the response occurred at the peak of the sine wave, regardless of the exact time of stimulation. The output of the neuron was therefore synchronized with the sine wave and depended only partly on the input phase. 4. These data demonstrate that neurons from the inferior olivary nucleus are capable of operating as accurate synchronizing devices. Moreover, by affecting the delay line, they act as a logic gate that ensures that the information will be added to the system only at given times.

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.

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