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Simulating structural plasticity of large scale networks in NEST

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Example of the definition of two synaptic elements for an integrate and fire neuron using PyNEST. Here, the growth curve of the synaptic element is a Gaussian defined by three parameters: υ (the growth rate), ηz and ε (intersections with the x-axis). The growth dynamic depends on the electrical activity of the neuron modeled by the intracellular concentration of calcium.
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Figure 1: Example of the definition of two synaptic elements for an integrate and fire neuron using PyNEST. Here, the growth curve of the synaptic element is a Gaussian defined by three parameters: υ (the growth rate), ηz and ε (intersections with the x-axis). The growth dynamic depends on the electrical activity of the neuron modeled by the intracellular concentration of calcium.

Mentions: Formation and deletion of synapses in the model for structural plasticity (MSP) [2] depends on the number of synaptic contact possibilities that each neuron has, i.e. the number of axonal boutons and dendritic spines. Therefore, we developed a framework that allows the addition of synaptic elements (i.e. axonal boutons or dendritic spines) for every neuron model already implemented in NEST. The user can then define its own synaptic elements and their corresponding growth dynamic depending on the electrical activity (see Figure 1). Synapses are formed by merging corresponding synaptic elements or are deleted when synaptic elements are lost. The update in connectivity depends on the availability of the synaptic elements in the entire networks. To make this model scalable for HPC, we developed a probabilistic approach that reduce both communication between compute nodes and their memory usage.


Simulating structural plasticity of large scale networks in NEST
Example of the definition of two synaptic elements for an integrate and fire neuron using PyNEST. Here, the growth curve of the synaptic element is a Gaussian defined by three parameters: υ (the growth rate), ηz and ε (intersections with the x-axis). The growth dynamic depends on the electrical activity of the neuron modeled by the intracellular concentration of calcium.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4126386&req=5

Figure 1: Example of the definition of two synaptic elements for an integrate and fire neuron using PyNEST. Here, the growth curve of the synaptic element is a Gaussian defined by three parameters: υ (the growth rate), ηz and ε (intersections with the x-axis). The growth dynamic depends on the electrical activity of the neuron modeled by the intracellular concentration of calcium.
Mentions: Formation and deletion of synapses in the model for structural plasticity (MSP) [2] depends on the number of synaptic contact possibilities that each neuron has, i.e. the number of axonal boutons and dendritic spines. Therefore, we developed a framework that allows the addition of synaptic elements (i.e. axonal boutons or dendritic spines) for every neuron model already implemented in NEST. The user can then define its own synaptic elements and their corresponding growth dynamic depending on the electrical activity (see Figure 1). Synapses are formed by merging corresponding synaptic elements or are deleted when synaptic elements are lost. The update in connectivity depends on the availability of the synaptic elements in the entire networks. To make this model scalable for HPC, we developed a probabilistic approach that reduce both communication between compute nodes and their memory usage.

View Article: PubMed Central - HTML

No MeSH data available.