Simulation of Large Scale Neural Models With Event-Driven 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 (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 execu

SUBMITTER: Azevedo Carvalho N 

PROVIDER: S-EPMC7591773 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature