.png){kind=icon}
.png){kind=icon}
Return
Larger GPU-accelerated brain simulations with procedural connectivity
.png){kind=icon}
OA
AI
DOI：10.1038/s43588-020-00022-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Simulations are an important tool for investigating brain function but large models are needed to faithfully reproduce the statistics and dynamics of brain activity. Simulating large spiking neural network models has, until now, needed so much memory for storing synaptic connections that it required high performance computer systems. Here, we present an alternative simulation method we call 'procedural connectivity' where connectivity and synaptic weights are generated 'on the fly' instead of stored and retrieved from memory. This method is particularly well suited for use on graphical processing units (GPUs)-which are a common fixture in many workstations. Using procedural connectivity and an additional GPU code generation optimization, we can simulate a recent model of the macaque visual cortex with 4.13 x 10(6) neurons and 24.2 x 10(9) synapses on a single GPU-a significant step forward in making large-scale brain modeling accessible to more researchers.
Keywords:
NETWORK
MODEL
CIRCUIT
Journal
IF:
18.3
Papers:
3.1K
Citations:
4.0K