Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks

Understand

The primate visual system has inspired the development of deep artificial neural networks, which have revolutionized the computer vision domain.

• Yet these networks are much less energy-efficient than their biological counterparts, and they are typically trained with backpropagation, which is extremely data-hungry.
• To address these limitations, we used a deep convolutional spiking neural network (DCSNN) and a latency-coding scheme.
• We trained it using a combination of spike-timing-dependent plasticity (STDP) for the lower layers and reward-modulated STDP (R-STDP) for the higher ones.