Mastering the Output Frequency in Spiking Neural Networks

Image recognition tasks require multi-layer networks to achieve good performance on complex data. However, building multi-layer spiking neural networks (SNN) still remains unreachable. One cause is that the learning mechanism of these models decreases the spiking activity throughout the layers. We propose three mechanisms to solve this issue without impacting the performance of the network: target frequency threshold adaptation, which forces neurons to reach a desired frequency, binary coding, which improves the performance of the network at high levels of activity, and mirrored STDP, which improves the convergence of the training. Experiments on single layer networks show that these mechanisms preserve both the recognition rate and the level of spiking activity.