A wake-sleep algorithm for recurrent, spiking neural networks
This addresses a specific bottleneck in training recurrent spiking neural networks for relational inference, offering an incremental improvement to avoid clustering and enhance learning efficiency.
The paper tackles the problem of attractor states in recurrent spiking neural networks using correlation-based learning, which reduces pattern representation and inference ability, by introducing a wake-sleep algorithm that increases learning rates up to tenfold and improves convergence speed and inference error.
We investigate a recently proposed model for cortical computation which performs relational inference. It consists of several interconnected, structurally equivalent populations of leaky integrate-and-fire (LIF) neurons, which are trained in a self-organized fashion with spike-timing dependent plasticity (STDP). Despite its robust learning dynamics, the model is susceptible to a problem typical for recurrent networks which use a correlation based (Hebbian) learning rule: if trained with high learning rates, the recurrent connections can cause strong feedback loops in the network dynamics, which lead to the emergence of attractor states. This causes a strong reduction in the number of representable patterns and a decay in the inference ability of the network. As a solution, we introduce a conceptually very simple "wake-sleep" algorithm: during the wake phase, training is executed normally, while during the sleep phase, the network "dreams" samples from its generative model, which are induced by random input. This process allows us to activate the attractor states in the network, which can then be unlearned effectively by an anti-Hebbian mechanism. The algorithm allows us to increase learning rates up to a factor of ten while avoiding clustering, which allows the network to learn several times faster. Also for low learning rates, where clustering is not an issue, it improves convergence speed and reduces the final inference error.