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Simulation of Large Scale Neural Models With Event-Driven Connectivity Generation

Nathalie Azevedo Carvalho 1, * Sylvain Contassot-Vivier 2 Laure Buhry 1 Dominique Martinez 1
* Corresponding author
1 NEUROSYS - Analysis and modeling of neural systems by a system neuroscience approach
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
2 SIMBIOT - SIMulating and Building IOT
LORIA - NSS - Department of Networks, Systems and Services
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 (HH) type neurons with event-driven updating of the synaptic currents. As the HH model is a continuous model, there is no explicit spike events. Thus, in order to preserve the accuracy of the integration method, a spike detection algorithm is developed that accurately determines spike times. This approach allows us to regenerate the outgoing connections at each event, thereby avoiding the storage of the connectivity. Consequently, memory consumption is significantly reduced while preserving execution time and accuracy of the simulations, especially the spike times of detailed point neuron models. The efficiency of the method, implemented in the SiReNe software, is demonstrated by the simulation of a striatum model which consists of more than 10⁶ neurons and 10⁸ synapses (each neuron has a fan-out of 504 post-synaptic neurons), under normal and Parkinson's conditions.
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Submitted on : Monday, December 7, 2020 - 10:45:45 AM
Last modification on : Wednesday, January 13, 2021 - 3:47:52 PM


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Nathalie Azevedo Carvalho, Sylvain Contassot-Vivier, Laure Buhry, Dominique Martinez. Simulation of Large Scale Neural Models With Event-Driven Connectivity Generation. Frontiers in Neuroinformatics, Frontiers, 2020, 14, pp.14. ⟨10.3389/fninf.2020.522000⟩. ⟨hal-03041616⟩



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