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Related Concept Videos

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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Long-term Potentiation01:25

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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
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The Synapse02:47

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Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.
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Chemical Synapses01:26

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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
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Neural Circuits01:25

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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.
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Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
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Related Experiment Video

Updated: Jun 27, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Spiking Neural Membrane Systems with Adaptive Synaptic Time Delay.

Yongshun Shen1, Xuefu Liu1, Zhen Yang1

  • 1College of Business, Shandong Normal University, Jinan 250014, P. R. China.

International Journal of Neural Systems
|May 6, 2024
PubMed
Summary
This summary is machine-generated.

Spiking neural P systems (SNP systems) were enhanced with adaptive synaptic time delay (ADSNP systems) by incorporating astrocytes. This model better simulates biological neural networks and proves Turing universal for computation.

Keywords:
Membrane computingmembrane systemsspiking neural P systemsturing universality

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Area of Science:

  • Computational neuroscience
  • Theoretical computer science
  • Biologically inspired computing

Background:

  • Spiking neural P systems (SNP systems) are computational models known for parallelism and time encoding.
  • Original SNP systems did not account for synaptic transmission delays, limiting their biological realism.
  • Adaptive regulation of synaptic delay is crucial for accurate time encoding in neural systems.

Purpose of the Study:

  • To propose and investigate Spiking Neural Membrane Systems with Adaptive Synaptic Time Delay (ADSNP systems).
  • To enhance computational models by incorporating astrocyte-mediated adaptive synaptic delays.
  • To demonstrate the Turing universality and practical feasibility of ADSNP systems.

Main Methods:

  • Introduction of astrocytes capable of generating Adenosine Triphosphate (ATP) into the SNP system framework.
  • Astrocytes convert received spikes into ATP, modulating synaptic time delays.
  • Mathematical proofs of Turing universality in number generating and accepting modes.

Main Results:

  • The Turing universality of ADSNP systems was formally proven.
  • A small universal ADSNP system with 93 neurons and astrocytes was constructed.
  • ADSNP systems demonstrated superiority over six existing variants.
  • A practical application for credit card fraud detection was successfully implemented.

Conclusions:

  • ADSNP systems offer a more biologically plausible model of neural information processing by including adaptive synaptic delays.
  • The enhanced model improves the adaptability and temporal control of spiking neural P systems.
  • ADSNP systems are feasible for real-world applications, such as fraud detection.