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Related Concept Videos

Neural Circuits01:25

Neural Circuits

3.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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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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Electrical Synapses01:28

Electrical Synapses

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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.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
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Overview of Synapses01:25

Overview of Synapses

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A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...
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The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

4.0K
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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¹H NMR: Long-Range Coupling01:27

¹H NMR: Long-Range Coupling

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The coupling interactions of nuclei across four or more bonds are usually weak, with J values less than 1 Hz. While these are usually not observed in spectra, the presence of multiple bonds along the coupling pathway can result in observable long-range coupling.
In alkenes, spin information is communicated via σ–π overlap, as seen in allylic (four-bond) and homoallylic (five-bond) couplings. These coupling interactions are stronger when the σ bond is parallel to the alkene...
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Related Experiment Video

Updated: Mar 2, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

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On strongly connected networks with excitable-refractory dynamics and delayed coupling.

P Grindrod1, T E Lee1

  • 1Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK.

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|May 10, 2017
PubMed
Summary

The human brain

Keywords:
computational simulationdecision-makinginvariant attracting toriphase lockingresonance

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Area of Science:

  • Computational neuroscience
  • Network neuroscience
  • Theoretical neuroscience

Background:

  • The human brain's neural architecture is complex.
  • Understanding its structure-function relationship is crucial for neuroscience.
  • Existing models often simplify neural connectivity.

Purpose of the Study:

  • To propose a novel directed graph model for neural architecture.
  • To investigate the computational properties of strongly connected sub-graphs (SCGs).
  • To explore the implications of SCG organization for cognitive functions.

Main Methods:

  • Developed a directed graph model of neural architecture.
  • Incorporated neuron excitable-refractory dynamics and transmission delays.
  • Numerically simulated SCGs of varying sizes and local structures.
  • Analyzed attractor dynamics and computational capacity.

Main Results:

  • SCGs exhibit attractors equivalent to continual winding maps on low-dimensional tori.
  • A larger number of smaller irreducible SCGs may enhance processing capacity and efficiency.
  • This architecture supports decision-making with partial or early information.

Conclusions:

  • The proposed SCG-based network model offers insights into brain architecture.
  • Brain evolution favoring numerous small SCGs could optimize cognitive functions.
  • This paradigm may underpin human cognition and decision-making processes.