High performance computing on the Graphics Processing Unit (GPU) is an emerging
field driven by the promise of high computational power at a low cost. However,
GPU programming is a non-trivial task and moreover architectural limitations
raise the question of whether investing effort in this direction may be
worthwhile. In this work, we use GPU programming to simulate a two-layer network
of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and
investigate its ability to learn a simplified navigation task using a
policy-gradient learning rule stemming from Reinforcement Learning. The purpose
of this paper is twofold. First, we want to support the use of GPUs in the field
of Computational Neuroscience. Second, using GPU computing power, we investigate
the conditions under which the said architecture and learning rule demonstrate
best performance. Our work indicates that networks featuring strong
Mexican-Hat-shaped recurrent connections in the top layer, where decision making
is governed by the formation of a stable activity bump in the neural population
(a “non-democratic” mechanism), achieve mediocre learning results at
best. In absence of recurrent connections, where all neurons “vote”
independently (“democratic”) for a decision via population vector
readout, the task is generally learned better and more robustly. Our study would
have been extremely difficult on a desktop computer without the use of GPU
programming. We present the routines developed for this purpose and show that a
speed improvement of 5x up to 42x is provided versus optimised Python code. The
higher speed is achieved when we exploit the parallelism of the GPU in the
search of learning parameters. This suggests that efficient GPU programming can
significantly reduce the time needed for simulating networks of spiking neurons,
particularly when multiple parameter configurations are investigated.