scholarly journals Regulation of AMPA and NMDA receptor-mediated EPSPs in dendritic trees of thalamocortical cells

2013 ◽  
Vol 109 (1) ◽  
pp. 13-30 ◽  
Author(s):  
Francis Lajeunesse ◽  
Helmut Kröger ◽  
Igor Timofeev

Two main excitatory synapses are formed at the dendritic arbor of first-order nuclei thalamocortical (TC) neurons. Ascending sensory axons primarily establish contacts at large proximal dendrites, whereas descending corticothalamic fibers form synapses on thin distal dendrites. With the use of a multicomparment computational model based on fully reconstructed TC neurons from the ventroposterolateral nucleus of the cat, we compared local responses at the site of stimulation as well as somatic responses induced by both α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)- and N-methyl-d-aspartate receptor (NMDAR)-mediated currents. We found that AMPAR-mediated responses, when synapses were located at proximal dendrites, induced a larger depolarization at the level of soma, whereas NMDAR-mediated responses were more efficient for synapses located at distal dendrites. The voltage transfer and transfer impedance were higher for NMDAR than for AMPAR activation at any location. For both types of synaptic current and for both input locations at the dendritic arbor, somatic responses were characterized by a low variability despite the large variability found in local responses in dendrites. The large neurons had overall smaller somatic responses than small neurons, but this relation was not found in local dendritic responses. We conclude that in TC cells, the dendritic location of small synaptic inputs does not play a major role in the amplitude of a somatic response, but the size of the neuron does. The variability of response amplitude between cells was much larger than the variability within cells. This suggests possible functional segregation of TC neurons of different size.

2008 ◽  
Vol 20 (7) ◽  
pp. 1717-1731 ◽  
Author(s):  
Xiaoshen Li ◽  
Giorgio A. Ascoli

The firing rate of individual neurons depends on the firing frequency of their distributed synaptic inputs, with linear and nonlinear relations subserving different computational functions. This letter explores the relationship between the degree of synchrony among excitatory synapses and the linearity of the response using detailed compartmental models of cortical pyramidal cells. Synchronous input resulted in a linear input-output relationship, while asynchronous stimulation yielded sub- and supraproportional outputs at low and high frequencies, respectively. The dependence of input-output linearity on synchrony was sigmoidal and considerably robust with respect to dendritic location, stimulus irregularity, and alteration of active and synaptic properties. Moreover, synchrony affected firing rate differently at lower and higher input frequencies. A reduced integrate-and-fire model suggested a mechanism explaining these results based on spatiotemporal integration, with fundamental implications relating synchrony to memory encoding.


F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 539
Author(s):  
Romain D. Cazé

Multiple studies have shown how dendrites enable some neurons to perform linearly non-separable computations. These works focus on cells with an extended dendritic arbor where voltage can vary independently, turning dendritic branches into local non-linear subunits. However, these studies leave a large fraction of the nervous system unexplored. Many neurons, e.g. granule cells, have modest dendritic trees and are electrically compact. It is impossible to decompose them into multiple independent subunits. Here, we upgraded the integrate and fire neuron to account for saturating dendrites. This artificial neuron has a unique membrane voltage and can be seen as a single layer. We present a class of linearly non-separable computations and how our neuron can perform them. We thus demonstrate that even a single layer neuron with dendrites has more computational capacity than without. Because any neuron has one or more layer, and all dendrites do saturate, we show that any dendrited neuron can implement linearly non-separable computations.


2007 ◽  
Vol 97 (6) ◽  
pp. 4023-4035 ◽  
Author(s):  
Giovanbattista Grande ◽  
Tuan V. Bui ◽  
P. Ken Rose

In the presence of monoamines, L-type Ca2+ channels on the dendrites of motoneurons contribute to persistent inward currents (PICs) that can amplify synaptic inputs two- to sixfold. However, the exact location of the L-type Ca2+ channels is controversial, and the importance of the location as a means of regulating the input-output properties of motoneurons is unknown. In this study, we used a computational strategy developed previously to estimate the dendritic location of the L-type Ca2+ channels and test the hypothesis that the location of L-type Ca2+ channels varies as a function of motoneuron size. Compartmental models were constructed based on dendritic trees of five motoneurons that ranged in size from small to large. These models were constrained by known differences in PIC activation reported for low- and high-conductance motoneurons and the relationship between somatic PIC threshold and the presence or absence of tonic excitatory or inhibitory synaptic activity. Our simulations suggest that L-type Ca2+ channels are concentrated in hotspots whose distance from the soma increases with the size of the dendritic tree. Moving the hotspots away from these sites (e.g., using the hotspot locations from large motoneurons on intermediate-sized motoneurons) fails to replicate the shifts in PIC threshold that occur experimentally during tonic excitatory or inhibitory synaptic activity. In models equipped with a size-dependent distribution of L-type Ca2+ channels, the amplification of synaptic current by PICs depends on motoneuron size and the location of the synaptic input on the dendritic tree.


2009 ◽  
Vol 101 (6) ◽  
pp. 3226-3234 ◽  
Author(s):  
Albert Gidon ◽  
Idan Segev

We explored in a computational study the effect of dendrites on excitatory synapses undergoing spike-timing–dependent plasticity (STDP), using both cylindrical dendritic models and reconstructed dendritic trees. We show that even if the initial strength, gpeak, of distal synapses is augmented in a location independent manner, the efficacy of distal synapses diminishes following STDP and proximal synapses would eventually dominate. Indeed, proximal synapses always win over distal synapses following linear STDP rule, independent of the initial synaptic strength distribution in the dendritic tree. This effect is more pronounced as the dendritic cable length increases but it does not depend on the dendritic branching structure. Adding a small multiplicative component to the linear STDP rule, whereby already strong synapses tend to be less potentiated than depressed (and vice versa for weak synapses) did partially “save” distal synapses from “dying out.” Another successful strategy for balancing the efficacy of distal and proximal synapses following STDP is to increase the upper bound for the synaptic conductance ( gmax) with distance from the soma. We conclude by discussing an experiment for assessing which of these possible strategies might actually operate in dendrites.


F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 539
Author(s):  
Romain D. Cazé

Multiple studies have shown how dendrites enable some neurons to perform linearly non-separable computations. These works focus on cells with an extended dendritic arbor where voltage can vary independently, turning dendritic branches into local non-linear subunits. However, these studies leave a large fraction of the nervous system unexplored. Many neurons, e.g. granule cells, have modest dendritic trees and are electrically compact. It is impossible to decompose them into multiple independent subunits. Here, we upgraded the integrate and fire neuron to account for saturating dendrites. This artificial neuron has a unique membrane voltage and can be seen as a single layer. We present a class of linearly non-separable computations and how our neuron can perform them. We thus demonstrate that even a single layer neuron with dendrites has more computational capacity than without. Because any neuron has one or more layer, and all dendrites do saturate, we show that any dendrited neuron can implement linearly non-separable computations.


2021 ◽  
Author(s):  
◽  
Fabio Sartori

Neurons are cells with a highly complex morphology; their dendritic arbor spans up to thousands of micrometers. This extended arbor poses a challenge for the logistics of neuronal processes: mRNA, proteins, and organelles have to be transported to dendrites, hundreds of micrometers away from the soma. This thesis aims to calculate the minimum number of proteins needed to populate the dendritic trees for different scenarios. In chapter 2, I analyzed the ability of different mechanisms to populate the dendritic arbor. I started from the solution of the diffusion equation in Sec. 2.1, then I included the contribution of active transport in Sec. 2.2 and showed how it could have either the effect of increasing the effective diffusion coefficient or of introducing a bias in the diffusion process. In Sec. 2.3 I studied the spatial distribution of locally synthesized protein, accordingly with actively and passively transported mRNA. In Sec. 2.5, I derived the boundary condition for branches showing a qualitatively different behavior of surface and cytoplasmic proteins induced by the medium’s dimensionality in which they diffuse. In chapter 3, I introduced the concept of protein requirement, defined as the minimum number of proteins that the neuron needs to produce to provide at least one protein to each micrometer of the dendritic arbor. In Sec. 3.1, I derived the protein requirement for diffusive proteins for somatic translation and constant translation in the dendritic arbor. In Sec. 3.2, I analyzed numerically the protein requirement in the case of actively transported protein synthesized in the soma, and, in Sec. 3.3, in the case of actively transported proteins synthesized in the dendritic arbor. In Sec. 3.4, I analyzed the protein requirement of protein synthesized in the dendrite accordingly with the distribution of mRNA described in Sec. 3.3 and 3.2. In Sec. 3.5, I derived the protein requirement for a single branch and purely diffusive proteins. In chapter 4, I analyzed the relation between the radii of the three afferent dendrites in a branch, their length, and the diffusion length of a protein. In Sec. 4.1 I derived the optimal ratio between the radii of the daughter dendrites that minimizes the protein requirement. In Sec. 4.3 I introduced the 3/2− Rall Rule and in Sec. 4.5 its generalization. Finally, I used those rules to estimate the fraction of proteins diffusing away from and toward the soma. In chapter 5, I analyzed the radii distribution for three categories of neurons: cultured hippocampal neurons in Sec. 5.1, stomatogastric ganglia neuron in Sec. 5.2, and 3DEM reconstructed prefrontal pyramidal neurons in Sec. 5.3. For each of these three classes, I analyzed the distribution of radii, Rall exponents, and the probability ratio. For most of them, I found that the probability of a protein diffusing away from the soma is higher for surface proteins than for cytoplasmic ones. I quantified this with a parameter called surface bias. In Chapter 6, I analyzed the fluorescent ratio imaged by our collaborators Anne-Sophie Hafner, for a surface protein, GFP::Nlg, and a soluble one, GFP, in cultured hippocampal neurons, and I compared the fluorescent ratio with the probability ratio obtained in 5.1, finding that they are in good agreement. In chapter 7, I compared the real dendritic morphologies imaged by one of our collaborators Ali Karimi with the optimal branching rule obtained in Sec. 4.1 and I calculated the cost for not having optimal branching radii. Finally, in Chapter 8, I used the knowledge of the branching statistics gathered in 5.3 to simulate the protein profile on three different classes of neurons: pyramidal neurons, granule neuron, and Purkinje neurons. I compared the protein profile for surface and cytoplasmic neurons for each morphology for two different values of the diffusion length: λ = 109µm and λ = 473µm, both for optimized radii and symmetrical radii. I showed how the radii optimization reduces the protein requirement of a factor 10 4 for pyramidal neurons.


Author(s):  
M. C. Whitehead

A fundamental problem in taste research is to determine how gustatory signals are processed and disseminated in the mammalian central nervous system. An important first step toward understanding information processing is the identification of cell types in the nucleus of the solitary tract (NST) and their synaptic relationships with oral primary afferent terminals. Facial and glossopharyngeal (LIX) terminals in the hamster were labelled with HRP, examined with EM, and characterized as containing moderate concentrations of medium-sized round vesicles, and engaging in asymmetrical synaptic junctions. Ultrastructurally the endings resemble excitatory synapses in other brain regions.Labelled facial afferent endings in the RC subdivision synapse almost exclusively with distal dendrites and dendritic spines of NST cells. Most synaptic relationships between the facial synapses and the dendrites are simple. However, 40% of facial endings engage in complex synaptic relationships within glomeruli containing unlabelled axon endings particularly ones termed "SP" endings. SP endings are densely packed with small, pleomorphic vesicles and synapse with both the facial endings and their postsynaptic dendrites by means of nearly symmetrical junctions.


Author(s):  
Kristen M. Harris

Dendritic spines are the tiny protrusions that stud the surface of many neurons and they are the location of over 90% of all excitatory synapses that occur in the central nervous system. Their small size and variable shapes has in large part made detailed study of their structure refractory to conventional light microscopy and single section electron microscopy (EM). Yet their widespread occurrence and likely involvement in learning and memory has motivated extensive efforts to obtain quantitative descriptions of spines in both steady state and dynamic conditions. Since the seminal mathematical analyses of D’Arcy Thompson, the power of establishing quantitatively key parameters of structure has become recognized as a foundation of successful biological inquiry. For dendritic spines highly precise determinations of structure and its variation are proving themselves as the kingpin for establishing a valid concept of function. The recent conjunction of high quality information about the structure, function, and theoretical implications of dendritic spines has produced a flurry of new considerations of their role in synaptic transmission.


Author(s):  
Trevor Simcox ◽  
Lauren Seo ◽  
Kevin Dunham ◽  
Shengnan Huang ◽  
Catherine Petchprapa ◽  
...  

Abstract Background The etiology of carpal tunnel syndrome (CTS) is multifactorial. Static mechanical characteristics of CTS have been described, but dynamic (muscular) parameters remain obscure. We believe that musculature overlying the transverse carpal ligament may have an effect on carpal tunnel pressure and may explain the prevalence of CTS in manual workers. Questions/Purposes To utilize magnetic resonance imaging (MRI) imaging to estimate the amount of muscle crossing the area of the carpal tunnel and to compare these MRI measurements in patients with and without documented CTS. Methods A case–control study of wrist MRI scans between January 1, 2018, and December 1, 2019, was performed. Patients with a diagnosis of CTS were matched by age and gender with controls without a diagnosis of CTS. Axial MRI cuts at the level of the hook of the hamate were used to measure the thenar and hypothenar muscle depth overlying the carpal tunnel. Muscle depth was quantified in millimeters at three points: midcapitate, capitate–hamate border, capitate–trapezoid border. Average depth was calculated by dividing the cross-sectional area (CSA) by the transverse carpal ligament width. Statistical analysis included Student's t-test, chi-square test, and Pearson's correlation coefficient calculation. Results A total of 21 cases and 21 controls met the inclusion criteria for the study. There were no significant differences in demographics between case and control groups. The location and depth of the musculature crossing the carpal tunnel were highly variable in all areas evaluated. A significantly positive correlation was found between proximal median nerve CSA and muscle depth in the capitate–hamate area (correlation coefficient = 0.375; p = 0.014). CSA was not significantly associated with chart documented CTS. Conclusions We found large variability in our measurements. This likely reflects true anatomical variation. The significance of our findings depends on the location of the muscles and the line of pull and their effect on the mechanics of the transverse carpal ligament. Future research will focus on refining measurement methodology and understanding the mechanical effect of the muscular structure and insertions on carpal tunnel pressure. Level of Evidence This is a Level 3, case–control study.


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