Event-driven simulation of large scale neural models with on-demand connectivity generation

Abstract : Accurate simulations of brain structures is a major problem in neuroscience. Many works are dedicated to design better models or to develop more efficient simulation schemes. In this paper, we propose a hybrid simulation scheme that combines time-stepping second-order integration of Hodgkin-Huxley type neurons with event-driven updating of the synaptic currents. Also, a spike detection method is developed to determine the spike time in order to preserve the accuracy of the integration method. This approach allows us to regenerate the outgoing connections at each event, thereby avoiding the storage of the connectivity. Consequently , memory consumption and execution time are significantly reduced while preserving accurate simulations, especially the spike times of detailed point neuron models. The efficiency of the method is demonstrated on the simulation of 10^6 interconnected neurons.
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Contributor : Nathalie Azevedo Carvalho <>
Submitted on : Thursday, January 9, 2020 - 2:35:38 PM
Last modification on : Monday, January 13, 2020 - 1:12:07 AM


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  • HAL Id : hal-02433782, version 1


Nathalie Azevedo Carvalho, Sylvain Contassot-Vivier, Laure Buhry, Dominique Martinez. Event-driven simulation of large scale neural models with on-demand connectivity generation. 2020. ⟨hal-02433782⟩



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