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Related Concept Videos

Neuroplasticity01:01

Neuroplasticity

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.
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.
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...
Plasticity00:58

Plasticity

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...
Graded Potential01:19

Graded Potential

Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or calcium...
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...

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3D Modeling of Dendritic Spines with Synaptic Plasticity
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Rate and pulse based plasticity governed by local synaptic state variables.

Christian G Mayr1, Johannes Partzsch

  • 1Endowed Chair of Highly Parallel VLSI Systems and Neural Microelectronics, Institute of Circuits and Systems, Faculty of Electrical Engineering and Information Science, University of Technology Dresden Dresden, Sachsen, Germany.

Frontiers in Synaptic Neuroscience
|March 23, 2011
PubMed
Summary

This study introduces a new Bienenstock-Cooper-Munroe (BCM) rule formulation for synaptic plasticity. It incorporates local synaptic dynamics, successfully replicating various experimental plasticity protocols with fewer parameters.

Keywords:
BCM/STDP synthesislocal state plasticityneuron dynamics based plasticityvoltage-based BCM

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Area of Science:

  • Computational Neuroscience
  • Synaptic Plasticity Modeling
  • Neuroscience

Background:

  • Traditional synaptic plasticity models (e.g., Bienenstock-Cooper-Munroe rule, spike timing-dependent plasticity) are primarily based on action potentials.
  • Emerging experimental evidence highlights the crucial role of local synaptic dynamics (e.g., membrane voltage, calcium levels, dendritic spikes) in plasticity.
  • Existing models often fail to fully capture the complexity of experimentally observed plasticity phenomena.

Purpose of the Study:

  • To introduce a novel formulation of the Bienenstock-Cooper-Munroe (BCM) rule.
  • To develop a plasticity model that integrates local postsynaptic membrane potential and presynaptic spike transmission dynamics.
  • To demonstrate the model's ability to replicate a wide range of experimental plasticity data using a parsimonious set of parameters.

Main Methods:

  • Developed a new BCM rule formulation based on instantaneous postsynaptic membrane potential and presynaptic spike transmission profiles.
  • Incorporated simple local voltage and current dynamics into the plasticity rule.
  • Validated the model by comparing its performance against diverse experimental plasticity protocols, including rate-based, timing-based, and voltage-dependent plasticity.

Main Results:

  • The proposed BCM rule formulation successfully replicates various experimental plasticity protocols, including rate, timing, and combined protocols.
  • The model demonstrates accurate prediction of voltage-dependent plasticity phenomena.
  • The new plasticity rule achieves high efficacy with a limited set of parameters, mitigating the risk of overfitting.

Conclusions:

  • The novel BCM rule formulation effectively captures essential local synaptic dynamics, offering a more comprehensive understanding of synaptic plasticity.
  • This model provides a powerful and parsimonious tool for simulating and analyzing diverse plasticity mechanisms observed in biological synapses.
  • The findings suggest that local synaptic dynamics play a critical role and can be effectively modeled without relying solely on spike timing or rates.