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Related Concept Videos

Integration of Synaptic Events01:28

Integration of Synaptic Events

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...
Long-term Potentiation01:25

Long-term Potentiation

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
LTP can occur when presynaptic neurons...
Long-term Potentiation01:35

Long-term Potentiation

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.
Neural Circuits01:25

Neural Circuits

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...
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

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.
Excitatory and Inhibitory Effects of Neurotransmitters01:29

Excitatory and Inhibitory Effects of Neurotransmitters

When an action potential reaches the presynaptic axon terminal, it releases neurotransmitters from the neuron into the synaptic cleft at a chemical synapse. The released neurotransmitter can be excitatory or inhibitory. The critical criteria commonly used to determine whether a molecule is a neurotransmitter at a chemical synapse are the molecule's presence in the presynaptic neuron. Second, its release is in response to strong presynaptic depolarization. And lastly, the presence of specific...

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Related Experiment Video

Updated: Jun 25, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

Association by synaptic facilitation in highly damped neural nets.

E M Harth1, S L Edgar

  • 1Department of Physics, Syracuse University, Syracuse, New York 13210, USA.

Biophysical Journal
|February 13, 2009
PubMed
Summary
This summary is machine-generated.

This study models cognitive functions using a neuron-like network, proposing synaptic facilitation as the basis for learning and memory. The network exhibits analogies to cerebral cortex association functions, like conditioned reflexes.

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Published on: October 18, 2013

Area of Science:

  • Computational neuroscience
  • Artificial neural networks
  • Cognitive modeling

Background:

  • Cognitive functions are complex processes.
  • Understanding the neural basis of cognition is a key scientific challenge.
  • Neural networks offer a framework for modeling cognitive functions.

Purpose of the Study:

  • To investigate cognitive functions within a homogeneous, randomly connected network of neuron-like elements.
  • To explore the role of synaptic facilitation in learning and memory within this model.
  • To identify analogies between the network's performance and known functions of the cerebral cortex.

Main Methods:

  • Utilizing a computational model of a randomly connected neural network.
  • Assuming information is encoded in the instantaneous firing states of neurons (off or on).
  • Employing a combination of mathematical analysis and computer simulation for data acquisition.

Main Results:

  • The model demonstrates no reverberations due to high damping.
  • The network's performance shows close analogies to association functions of the cerebral cortex.
  • Specific analogies include various types of conditioned reflexes.

Conclusions:

  • Synaptic facilitation can serve as a basis for learning and memory in artificial neural systems.
  • The model provides insights into potential mechanisms underlying cognitive functions.
  • The simulated biological entity represents a limited component of the cerebral cortex.