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Related Concept Videos

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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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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.
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A unified computational model for cortical post-synaptic plasticity.

Tuomo Mäki-Marttunen1, Nicolangelo Iannella2, Andrew G Edwards1

  • 1Simula Research Laboratory, Oslo, Norway.

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|July 31, 2020
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Summary
This summary is machine-generated.

This study presents a biochemical model of post-synaptic plasticity, integrating key signaling pathways to explain neocortical plasticity. The model accurately predicts neuromodulator-gated plasticity and protein dependencies, aiding research into mental disorders.

Keywords:
LTP/LTDSTDPbiochemically detailed modelcalcium signallingcomputational biologyintracellular signalling cascademouseneuroscienceratsystems biology

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Biochemistry

Background:

  • Understanding neocortical plasticity requires integrating multiple biochemical signaling pathways.
  • Existing models lack a unified picture of how pathways like CaMKII, PKA, and PKC collectively influence synaptic plasticity.

Purpose of the Study:

  • To develop a biochemically detailed computational model of post-synaptic plasticity.
  • To elucidate the collective contribution of CaMKII, PKA, and PKC pathways to synaptic potentiation and depression.
  • To link biochemical signaling to synaptic conductance via an AMPA-receptor-tetramer model.

Main Methods:

  • Constructed a detailed biochemical model of post-synaptic plasticity.
  • Incorporated Calcium/calmodulin-dependent protein kinase II (CaMKII), Protein Kinase A (PKA), and Protein Kinase C (PKC) pathways.
  • Developed a statistical model for AMPA-receptor-tetramers to estimate maximal synaptic conductance.

Main Results:

  • The model successfully reproduces neuromodulator-gated spike-timing-dependent plasticity observed in the visual cortex.
  • The model can be adapted to fit data from various cortical areas, identifying specific pathway contributions.
  • Demonstrated how protein availability influences different forms of plasticity.

Conclusions:

  • The developed model provides a unified framework for understanding neocortical plasticity.
  • It highlights the specific roles of CaMKII, PKA, and PKC in synaptic potentiation and depression.
  • The model serves as a valuable tool for investigating mental disorder-associated impairments in cortical plasticity.