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Related Concept Videos

Neuroplasticity01:01

Neuroplasticity

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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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Long-term Potentiation01:25

Long-term Potentiation

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

Long-term Potentiation

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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.
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Plasticity00:58

Plasticity

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Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...
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Neural Circuits01:25

Neural Circuits

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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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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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Related Experiment Video

Updated: Mar 19, 2026

Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording
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Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording

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Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight

Naoki Hiratani1, Tomoki Fukai2

  • 1Department of Complexity Science and Engineering, The University of TokyoKashiwa, Japan; Laboratory for Neural Circuit Theory, RIKEN Brain Science InstituteWako, Japan.

Frontiers in Neural Circuits
|June 16, 2016
PubMed
Summary
This summary is machine-generated.

Synaptic plasticity and wiring plasticity combine to create advantageous neural network structures. This rewiring is essential for reliable brain computation and efficient information transmission in local circuits.

Keywords:
computational modelconnectomicsneural decodingsynaptic plasticitysynaptogenesis

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • The adult mammalian cortex exhibits daily creation and elimination of synaptic connections (spines), forming nonrandom structures.
  • The functional advantage of specific synaptic connection structures and the necessity of spine dynamics beyond synaptic weight plasticity remain unclear.

Purpose of the Study:

  • To investigate whether a particular synaptic connection structure offers functional benefits in local circuits.
  • To understand why synaptic connection dynamics are necessary alongside synaptic weight plasticity.

Main Methods:

  • Theoretical analysis of an inference task model.
  • Numerical simulations combining Hebbian-type synaptic weight plasticity and wiring plasticity.

Main Results:

  • A robustly beneficial network structure naturally emerges from the combination of synaptic weight plasticity and wiring plasticity.
  • Wiring plasticity enables reliable computation and efficient information transmission, particularly in sparsely connected networks.
  • The proposed model reproduces the experimentally observed correlation between spine dynamics and task performance.

Conclusions:

  • The interplay of synaptic weight and wiring plasticity is crucial for forming advantageous neural network structures.
  • Spine dynamics are essential for reliable computation and efficient information processing in cortical circuits.
  • This model provides a framework for understanding the functional role of synaptic structural dynamics in learning and cognition.