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In machine learning, error back-propagation in multi-layer neural networks(deep learning) has been impressively successful in supervised andreinforcement learning tasks. As a model for learning in the brain, however,deep learning has long been regarded as implausible, since it relies in itsbasic form on a non-local plasticity rule. To overcome this problem,energy-based models with local contrastive Hebbian learning were proposed andtested on a classification task with networks of rate neurons. We extended thiswork by implementing and testing such a model with networks of leakyintegrate-and-fire neurons. Preliminary results indicate that it is possible tolearn a non-linear regression task with hidden layers, spiking neurons and alocal synaptic plasticity rule.

Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity