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Article Dans Une Revue The ReScience journal Année : 2018

[Re] Non-Additive Coupling Enables Propagation of Synchronous Spiking Activity in Purely Random Networks

Résumé

Dendritic non-linearities increase neurons’ computation capacity, turning them intocomplex computing units [4]. However, network studies are usually based on point neuron models that do not incorporate dendrites and their non-linearities. In contrast, the study replicated here [2] uses a simple point-neuron model that contains an effective description of dendrites by a non-linear summation of its excitatory synaptic input. Due to the simplicity of the model, both large-scale parameter exploration of a medium-sized network, as well as an analytical investigation of its properties are feasible. The original study was based on simulation and analysis code in C and Mathematica, but this code is not publicly available. Here, we replicate the study using the neural simulator Brian 2 [1,6], a simulator based on the Python language that has become a common choice in computational neuroscience [3]. This simulator offers a good trade-off between flexibility and performance and is therefore a suitable choice for this study of a non-standard neuron model.
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Dates et versions

hal-01856930 , version 1 (13-08-2018)

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Romain Cazé, Marcel Stimberg, Benoît Girard. [Re] Non-Additive Coupling Enables Propagation of Synchronous Spiking Activity in Purely Random Networks. The ReScience journal, 2018, 4 (1), ⟨10.5281/zenodo.1246659⟩. ⟨hal-01856930⟩
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