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Related Concept Videos

Integration of Synaptic Events01:28

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Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
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Graded Potential01:19

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Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
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Postsynaptic Potential (PSP)01:32

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Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
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Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
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Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo
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Exact Analysis of the Subthreshold Variability for Conductance-Based Neuronal Models with Synchronous Synaptic

Logan A Becker1,2, Baowang Li1,2,3,4,5, Nicholas J Priebe1,2,4

  • 1Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA.

Physical Review. X
|June 24, 2024
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Summary
This summary is machine-generated.

Neocortical neurons show variability. This study develops a framework to analyze subthreshold voltage variability, finding realistic variability requires strong synaptic drive or weak input synchrony, challenging asynchronous state theories.

Keywords:
Biological PhysicsComplex SystemsInterdisciplinary Physics

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Theoretical Neuroscience

Background:

  • Neocortical neuron activity displays significant variability, even with identical stimuli.
  • The asynchronous state hypothesis suggests independent neuronal firing, minimizing synchronous synaptic input.
  • Existing models explain spiking variability but not subthreshold membrane potential variability.

Purpose of the Study:

  • To develop an analytical framework for quantifying subthreshold voltage variability in conductance-based neurons.
  • To investigate the impact of synaptic input synchrony on neuronal variability.
  • To determine conditions for realistic subthreshold variability in neural networks.

Main Methods:

  • Utilized the theory of exchangeability to model input synchrony using jump-process-based synaptic drives.
  • Performed a moment analysis on the stationary response of a simplified neuronal model (all-or-none conductances, neglecting postspiking reset).
  • Derived exact, closed-form expressions for the first two stationary moments of membrane voltage.

Main Results:

  • The asynchronous regime produces realistic subthreshold variability (4-9 mV²) only with a limited number of strong synapses, consistent with thalamic input.
  • Achieving realistic variability with dense cortico-cortical inputs necessitates incorporating weak, non-zero input synchrony.
  • Neural variability diminishes to zero without synchrony in scaling limits with vanishing synaptic weights, irrespective of balanced state hypotheses.

Conclusions:

  • Subthreshold voltage variability in neocortical neurons is sensitive to synaptic input characteristics, including synchrony.
  • Realistic subthreshold variability may depend on specific network structures (e.g., strong thalamic drive) or weak input synchrony.
  • The findings challenge the theoretical underpinnings of mean-field theories for the asynchronous state in neural networks.