Towards biologically plausible learning in neural networks

Artificial neural networks are inspired by information processing performed by neural circuits in biology. While existing models are sufficient to solve many real-world tasks, they are far from reaching the potential of biological neural networks. These models are oversimplifications of their biological counterparts, omitting key features such as the spiking nature of their units or the locality during learning, among others. In this work, we, first, provide a short review of the most recent theories on biologically plausible learning and learning in Spiking Neural Networks. Then, aiming to give a step towards brain-inspired deep learning, we introduce a novel biologically plausible learning method. This approach achieves learning using only local information to each synapse, spiking units and unidirectional synaptic connections. We also propose a local solution to address the credit assignment problem based on target propagation. Finally, we evaluate our approach over three different tasks, i.e. boolean problems, image autoencoding and handwritten digit recognition.