GPU facilitated unsupervised visual feature acquisition in spiking neural networks

This paper demonstrates that feature acquisition systems composed of spiking neurons trained by spike-timing-dependent plasticity (STDP) can effectively be scaled using General-Purpose computing on Graphics Processing Units (GPGPU). Previous studies have demonstrated the efficacy of such systems for classes with low intra-class variability. Parallelization substantially increases the range of classes to which such a system can be applied. This system uses a hierarchical design based on the ventral visual pathway, alternating between selective layers that combine inputs and invariance layers that sample from inputs. Most systems do not, however, use spiking neurons as the brain does. Masquelier and Thorpe presented a system that produces highly informative features using a spiking convolutional network trained by STDP. However, this approach is costly in time and consequently is difficult to scale to a large library of internally complex features. Specifically, they reported that their system was incapable of learning the class “animal”, which was a major goal of that study. The present paper demonstrates that GPGPU parallelism can be leveraged to overcome the scaling limitations of the serial version. Highly informative features can then be generated in large quantities. The maximum complexity of classes learnable by this system is increased to encompass the natural class “animal” by GPGPU paralleization.