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Related Concept Videos

Action Potential01:14

Action Potential

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Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
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Propagation of Action Potentials01:23

Propagation of Action Potentials

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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
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Integration of Synaptic Events01:28

Integration of Synaptic Events

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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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The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
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Graded Potential01:19

Graded Potential

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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.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
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Action Potential: Phases of Stimulation01:28

Action Potential: Phases of Stimulation

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The action potential is a complex electrical event that occurs in excitable cells, such as neurons and muscle cells. It consists of several distinct phases, each with specific characteristics.
Resting Phase:
In this phase, the cell's membrane is at its resting potential, typically around -70 millivolts (mV) for neurons. Inside the cell, there is a higher concentration of potassium ions (K+) and a lower concentration of sodium ions (Na+). Voltage-gated sodium channels are closed, and...
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Related Experiment Video

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

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Generalized activity equations for spiking neural network dynamics.

Michael A Buice1, Carson C Chow

  • 1Modeling, Analysis and Theory Team, Allen Institute for Brain Science Seattle, WA, USA.

Frontiers in Computational Neuroscience
|December 4, 2013
PubMed
Summary
This summary is machine-generated.

Simulating spiking neural networks is computationally expensive. This study introduces a method to improve computational efficiency by incorporating spiking correlations into mean field models, offering a more tractable approach.

Keywords:
correlationsfinite size networksfluctuationsfokker-planckmean field theorypopulation ratetheta modelwilson-cowan model

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Mathematical modeling

Background:

  • Spiking neural networks (SNNs) show great computational potential.
  • Simulating SNNs is computationally intensive due to disparate timescales, posing a "stiff problem" in numerical analysis.
  • Analytical studies of spiking neurons are challenging.

Purpose of the Study:

  • To develop a more computationally tractable approach for studying spiking neural networks.
  • To augment existing mean field activity models with information about spiking correlations.
  • To create a generalized activity model that self-consistently includes spiking rates and correlations.

Main Methods:

  • Constructing a complete formal probabilistic description of the spiking neural network.
  • Employing perturbation expansion around a small parameter (e.g., inverse of network size).
  • Deriving a mean field theory that provides a rate-like description.

Main Results:

  • The developed mean field theory yields a rate-like description of the network.
  • First-order terms in the perturbation expansion accurately capture covariances.
  • The approach offers a way to incorporate spiking correlations into network activity models.

Conclusions:

  • The proposed method enhances the tractability of spiking neural network simulations.
  • Augmenting mean field models with correlation information provides a richer description of network dynamics.
  • This work bridges the gap between detailed spiking neuron simulations and simplified rate-based models.