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Related Concept Videos

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
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The resting membrane potential of a neuron (-70mV) is sustained due to the selective ion permeability of the membrane. At the resting potential, the membrane is slightly permeable to ions like sodium (Na+) and chloride (Cl−) and highly permeable to potassium ions (K+). Differences in the ions' concentration inside the cell compared to the outside are maintained by membrane transport proteins like channels and pumps.
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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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A Model of Fast Hebbian Spike Latency Normalization.

Hafsteinn Einarsson1, Marcelo M Gauy1, Johannes Lengler1

  • 1Department of Computer Science, Institute of Theoretical Computer Science, ETH ZurichZurich, Switzerland.

Frontiers in Computational Neuroscience
|May 31, 2017
PubMed
Summary

Hebbian learning can cause neural network instability. A new metaplasticity rule stabilizes neuronal input weights, preventing runaway excitation during asynchronous firing states.

Keywords:
STDPhomeostasismetaplasticityoscillationssynapse memorysynchrony

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Theoretical Neuroscience

Background:

  • Hebbian learning strengthens synaptic connections based on correlated neuronal activity, potentially leading to network instability.
  • Fast homeostatic mechanisms are hypothesized to prevent runaway excitation in neural networks, but their theoretical basis and biological implementation remain unclear.

Purpose of the Study:

  • To investigate a Hebbian metaplasticity rule for stabilizing neuronal activity.
  • To explore the theoretical underpinnings and computational implications of fast homeostatic mechanisms in neural networks.

Main Methods:

  • Analytical and computational modeling of neural network dynamics.
  • Simulations incorporating a Hebbian spike-timing-dependent metaplasticity rule.

Main Results:

  • Demonstrated that the proposed metaplasticity rule leads to inherently stable and rapid tuning of single-neuron input weights.
  • Showed the rule's effectiveness in asynchronous neural firing scenarios, including UP and DOWN states.

Conclusions:

  • A Hebbian spike-timing-dependent metaplasticity rule provides a viable mechanism for fast stabilization of neuronal input weights.
  • This mechanism addresses the potential instability arising from Hebbian learning, particularly in networks with asynchronous activity patterns.