Towards the Simulation of a Realistic Large-Scale Spiking Network on a Desktop Multi-GPU System

Abstract

The reproduction of the brain 'sactivity and its functionality is the main goal of modern neuroscience. To this aim, several models have been proposed to describe the activity of single neurons at different levels of detail. Then, single neurons are linked together to build a network, in order to reproduce complex behaviors. In the literature, different network-building rules and models have been described, targeting realistic distributions and connections of the neurons. In particular, the Granular layEr Simulator (GES) performs the granular layer network reconstruction considering biologically realistic rules to connect the neurons. Moreover, it simulates the network considering the Hodgkin-Huxley model. The work proposed in this paper adopts the network reconstruction model of GES and proposes a simulation module based on Leaky Integrate and Fire (LIF) model. This simulator targets the reproduction of the activity of large scale networks, exploiting the GPU technology to reduce the processing times. Experimental results show that a multi-GPU system reduces the simulation of a network with more than 1.8 million neurons from approximately 54 to 13 h.

Keywords: brain modeling; cerebellar network simulation; graphics processing units; high performance computing.

Figure 1

Feedforward and feedback loops. In the first loop, the MF excites the GRCs (yellow spheres), then the signals travel along the parallel fibers and excite the GOC (green sphere) that inhibits the GRCs. In the second loop, the MF excites the GRCs and the GOC, which later inhibits the GRCs. For both the loops, the excitatory signals are shown with red arrows, while the inhibitory ones are represented with blue arrows.

Figure 2

The upper part of the figure shows the circuital model. The lower part represents the voltage of the capacitor $C_m$ (blue line) in response to a constant current $I_E$. It also shows the voltage reset mechanism: when the $V_m$ reaches the threshold $V_{TH}$ (red dashed line), its value is instantly changed to the initial one ($V_{init}$) and it is maintained for a time equal to $t_{ref}$.

Figure 3

The results produced by the single cells C simulators.

Figure 4

The flowchart of the serial network simulator.

Figure 5

The flowchart of the GPU-based network simulator.

Figure 6

Chart of the processing time of the Network3 when stimulated with Prot4. The processing time is represented in logarithmic scale.