Unsupervised Learning in Synaptic Sampling Machines

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce the Synaptic Sampling Machine (SSM), a stochastic neural network model that uses synaptic unreliability as a means to stochasticity for sampling. Synaptic unreliability plays the dual role of an efficient mechanism for sampling in neuromorphic hardware, and a regularizer during learning akin to DropConnect. Similar to the original formulation of Boltzmann machines, the SSM can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. The SSM is trained to learn generative models with a synaptic plasticity rule implementing an event-driven form of contrastive divergence. We demonstrate this by learning a model of MNIST hand-written digit dataset, and by testing it in recognition and inference tasks. We find that SSMs outperform restricted Boltzmann machines (4.4% error rate vs. 5%), they are more robust to overfitting, and tend to learn sparser representations. SSMs are remarkably robust to weight pruning: removal of more than 80% of the weakest connections followed by cursory re-learning causes only a negligible performance loss on the MNIST task (4.8% error rate). These results show that SSMs offer substantial improvements in terms of performance, power and complexity over existing methods for unsupervised learning in spiking neural networks, and are thus promising models for machine learning in neuromorphic execution platforms.