“…On graphical cards with CUDA, the connectivity is stored in the compressed sparse row (CSR) format, where the values of each attribute are flattened into a single vector and a list of row pointers allow to attribute portions of this array to a single post-synaptic neuron (see Brette and Goodman, 2011 , for a review). These different data structures lead to a better parallel performance: CSR representations ensure a coalesced access to the attributes (i.e., the data is contiguous in memory), which is a strong condition for GPU computations to be efficient (Brette and Goodman, 2012 ), while the LIL structure allows a faster distribution of the data to the different OpenMP threads (Dinkelbach et al, 2012 ). LIL and CSR representations have similar memory requirements, but LIL is more adapted to the dynamical addition or suppression of synapses: structural plasticity is very inefficient on the GPU platform and is currently disabled.…”