Dendritic D‐type potassium currents inhibit the spike afterdepolarization in rat hippocampal CA1 pyramidal neurons

Metz, Alexia E.; Spruston, Nelson; Martina, Marco

doi:10.1113/jphysiol.2006.127068

Cited by 59 publications

(59 citation statements)

References 84 publications

(155 reference statements)

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“…This reversibility contrasts with a recent approach using a laser to permanently remove the axon (20). Third, being a physical method relying on the occlusion or removal of the dendrite, it allows both passive (particularly capacitive) and active (voltage-gated) contributions of the dendrites to be determined, in contrast to pharmacological (21) and electronic (22) approaches.…”

Section: Discussionmentioning

confidence: 94%

Targeted dendrotomy reveals active and passive contributions of the dendritic tree to synaptic integration and neuronal output

Bekkers

Häusser

2007

Proc. Natl. Acad. Sci. U.S.A.

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Neurons typically function as transduction devices, converting patterns of synaptic inputs, received on the dendrites, into trains of output action potentials in the axon. This transduction process is surprisingly complex and has been proposed to involve a twoway dialogue between axosomatic and dendritic compartments that can generate mutually interacting regenerative responses. To manipulate this process, we have developed a new approach for rapid and reversible occlusion or amputation of the primary dendrites of individual neurons in brain slices. By applying these techniques to cerebellar Purkinje and layer 5 cortical pyramidal neurons, we show directly that both the active and passive properties of dendrites differentially affect firing in the axon depending on the strength of stimulation. For weak excitation, dendrites act as a passive electrical load, raising spike threshold and dampening axonal excitability. For strong excitation, dendrites contribute regenerative inward currents, which trigger burst firing and enhance neuronal excitability. These findings provide direct support for the idea that dendritic morphology and conductances act in concert to regulate the excitability of the neuron.ost of the synaptic inputs to neurons are received on the dendritic tree, whereas the output signal of the neuron, the action potential (AP), is normally initiated in the axon, which originates from the soma. The input-output relationship of neurons depends not only on the individual properties of the axon and dendrites, but also on the interaction between them. It is essential to understand this interaction to determine its effect on the neuron's AP output.Recent advances in electrophysiological, immunocytochemical, and imaging techniques have shown that dendrites are electrically excitable and express a wide range of voltage-gated channels in a nonuniform and cell-specific manner (1, 2). In addition to supporting active backpropagation of Na APs from the axon, dendrites can initiate their own Na and Ca spikes under some circumstances (3, 4). However, dendrites also express high densities of voltage-gated K channels, which can dampen excitability (5, 6). These findings have raised the question of how this mixture of active currents affects the excitability of the soma and axon, and thus the output of the neuron. This question is complicated by the fact that the interaction between soma/axon and dendrites is mutual, even on short time scales. For example, in layer 5 pyramidal cells, single backpropagating APs lower the threshold for initiation of dendritic Ca spikes, which can in turn deliver a depolarizing voltage envelope to the soma that enhances AP firing (7).One approach to studying the contribution of dendritic electrogenesis to axonal output is to locally apply channel blockers, like tetrodotoxin or nickel (8, 9). When used in brain slices, this method suffers from uncertainties about the specificity and localization of the block. Another approach is to study the behavior of isolated somata, where the dendrites ...

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Section: Discussionmentioning

confidence: 94%

Targeted dendrotomy reveals active and passive contributions of the dendritic tree to synaptic integration and neuronal output

Bekkers

Häusser

2007

Proc. Natl. Acad. Sci. U.S.A.

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“…Yet even the reduced model presented here is under-constrained, as many configurations of channel densities and properties will give rise to the same observed response in the soma. Some authors have used multiple simultaneous somatic and dendritic recordings to refine computational models (Keren et al, 2005;Metz et al, 2007;Keren et al, 2009); ideally, one would want to observe each compartment's response in isolation.…”

Section: Discussionmentioning

confidence: 99%

Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data

Bahl

Stemmler

Herz

et al. 2012

Journal of Neuroscience Methods

124

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The construction of compartmental models of neurons involves tuning a set of parameters to make the model neuron behave as realistically as possible. While the parameter space of single-compartment models or other simple models can be exhaustively searched, the introduction of dendritic geometry causes the number of parameters to balloon. As parameter tuning is a daunting and time-consuming task when performed manually, reliable methods for automatically optimizing compartmental models are desperately needed, as only optimized models can capture the behavior of real neurons. Here we present a three-step strategy to automatically build reduced models of layer 5 pyramidal neurons that closely reproduce experimental data. First, we reduce the pattern of dendritic branches of a detailed model to a set of equivalent primary dendrites. Second, the ion channel densities are estimated using a multi-objective optimization strategy to fit the voltage trace recorded under two conditions - with and without the apical dendrite occluded by pinching. Finally, we tune dendritic calcium channel parameters to model the initiation of dendritic calcium spikes and the coupling between soma and dendrite. More generally, this new method can be applied to construct families of models of different neuron types, with applications ranging from the study of information processing in single neurons to realistic simulations of large-scale network dynamics.

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“…(Magee and Johnston, 2005; Metz et al, 2007; Pongs, 2008; Remy et al, 2010). While we have a good grasp on the mechanics of K v channels and their influence on the electrical properties of the cell membrane, little is known about posttranscriptional mechanisms that allow them to respond accordingly to changes in their extracellular environment.…”

Section: Introductionmentioning

confidence: 99%

mTORC1 Is a Local, Postsynaptic Voltage Sensor Regulated by Positive and Negative Feedback Pathways

Niere

Raab‐Graham

2017

Front. Cell. Neurosci.

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The mammalian/mechanistic target of rapamycin complex 1 (mTORC1) serves as a regulator of mRNA translation. Recent studies suggest that mTORC1 may also serve as a local, voltage sensor in the postsynaptic region of neurons. Considering biochemical, bioinformatics and imaging data, we hypothesize that the activity state of mTORC1 dynamically regulates local membrane potential by promoting and repressing protein synthesis of select mRNAs. Our hypothesis suggests that mTORC1 uses positive and negative feedback pathways, in a branch-specific manner, to maintain neuronal excitability within an optimal range. In some dendritic branches, mTORC1 activity oscillates between the “On” and “Off” states. We define this as negative feedback. In contrast, positive feedback is defined as the pathway that leads to a prolonged depolarized or hyperpolarized resting membrane potential, whereby mTORC1 activity is constitutively on or off, respectively. We propose that inactivation of mTORC1 increases the expression of voltage-gated potassium alpha (Kv1.1 and 1.2) and beta (Kvβ2) subunits, ensuring that the membrane resets to its resting membrane potential after experiencing increased synaptic activity. In turn, reduced mTORC1 activity increases the protein expression of syntaxin-1A and promotes the surface expression of the ionotropic glutamate receptor N-methyl-D-aspartate (NMDA)-type subunit 1 (GluN1) that facilitates increased calcium entry to turn mTORC1 back on. Under conditions such as learning and memory, mTORC1 activity is required to be high for longer periods of time. Thus, the arm of the pathway that promotes syntaxin-1A and Kv1 protein synthesis will be repressed. Moreover, dendritic branches that have low mTORC1 activity with increased Kv expression would balance dendrites with constitutively high mTORC1 activity, allowing for the neuron to maintain its overall activity level within an ideal operating range. Finally, such a model suggests that recruitment of more positive feedback dendritic branches within a neuron is likely to lead to neurodegenerative disorders.

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Dendritic D‐type potassium currents inhibit the spike afterdepolarization in rat hippocampal CA1 pyramidal neurons

Cited by 59 publications

References 84 publications

Targeted dendrotomy reveals active and passive contributions of the dendritic tree to synaptic integration and neuronal output

Targeted dendrotomy reveals active and passive contributions of the dendritic tree to synaptic integration and neuronal output

Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data

mTORC1 Is a Local, Postsynaptic Voltage Sensor Regulated by Positive and Negative Feedback Pathways

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