Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Long-term Potentiation01:25

Long-term Potentiation

3.8K
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...
3.8K
Long-term Potentiation01:35

Long-term Potentiation

59.1K
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.
59.1K
Neuroplasticity01:01

Neuroplasticity

2.2K
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.
2.2K
Plasticity00:58

Plasticity

3.2K
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...
3.2K
Integration of Synaptic Events01:28

Integration of Synaptic Events

5.3K
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...
5.3K
Long-term Depression01:03

Long-term Depression

3.5K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over...
3.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Representational similarity modulates neural and behavioral signatures of novelty.

Neuron·2026
Same author

Linking neural manifolds to circuit structure in recurrent networks.

Neuron·2026
Same author

Biologically informed cortical models predict optogenetic perturbations.

eLife·2026
Same author

Actor-critic networks with analogue memristors mimicking reward-based learning.

Nature machine intelligence·2025
Same author

Two-factor synaptic consolidation reconciles robustness with pruning and homeostatic scaling.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Novelty as a drive of human exploration in complex stochastic environments.

Proceedings of the National Academy of Sciences of the United States of America·2025

Related Experiment Video

Updated: Mar 8, 2026

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus
05:01

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus

Published on: September 20, 2024

841

Hebbian plasticity requires compensatory processes on multiple timescales.

Friedemann Zenke1, Wulfram Gerstner2

  • 1Department of Applied Physics, Stanford University, Stanford, CA 94305, USA fzenke@stanford.edu.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|January 18, 2017
PubMed
Summary

Neural circuits require both rapid and slow plasticity mechanisms for stability and learning. This study proposes rapid compensatory processes (RCPs) alongside slow homeostatic plasticity to resolve modeling paradoxes.

Keywords:
Hebbian plasticityheterosynaptic plasticityhomeostasismetaplasticityrapid compensatory processessynaptic scaling

More Related Videos

Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording
14:27

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

Published on: August 11, 2019

13.5K
Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
11:56

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

16.4K

Related Experiment Videos

Last Updated: Mar 8, 2026

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus
05:01

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus

Published on: September 20, 2024

841
Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording
14:27

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

Published on: August 11, 2019

13.5K
Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
11:56

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

16.4K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Hebbian plasticity models often lack stability due to rapid compensatory processes (RCPs).
  • Experimental evidence shows homeostatic plasticity is slow, contrasting with rapid compensatory processes (RCPs) in models.
  • Simulations with slow homeostatic plasticity alone fail to maintain network stability without additional controls.

Purpose of the Study:

  • To resolve the paradox between slow experimental homeostatic plasticity and rapid compensatory processes (RCPs) in models.
  • To propose a unified framework integrating diverse plasticity mechanisms for neural circuit stability.
  • To explain how Hebbian and homeostatic plasticity interact to support learning and memory.

Main Methods:

  • Review of theoretical and experimental research on Hebbian and homeostatic plasticity.
  • Analysis of mathematical models of synaptic plasticity.
  • Conceptual integration of rapid and slow plasticity mechanisms.

Main Results:

  • Slow homeostatic plasticity alone is insufficient for stabilizing Hebbian plasticity.
  • Rapid compensatory processes (RCPs), such as heterosynaptic depression, stabilize plasticity on short timescales.
  • Slower homeostatic plasticity fine-tunes neural circuits.

Conclusions:

  • Neural circuits utilize an interplay of plasticity mechanisms across different timescales.
  • Both rapid compensatory processes (RCPs) and slow homeostatic plasticity are crucial for network stability and function.
  • Learning and memory emerge from the synergistic action of diverse, time-scaled plasticity mechanisms.