Deep learning in spiking neural networks

Training Deep Spiking Neural Networks Using Backpropagation

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signa...

Learning in large-scale spiking neural networks

Learning is central to the exploration of intelligence. Psychology and machine learning provide high-level explanations of how rational agents learn. Neuroscience provides lowlevel descriptions of how the brain changes as a result of learning. This thesis attempts to bridge the gap between these two levels of description by solving problems using machine learning ideas, implemented in biologica...

Supervised learning in multilayer spiking neural networks

We introduce a supervised learning algorithm for multilayer spiking neural networks. The algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons firing multiple spikes in artificial neural networks with hidden layers. It can also, in principle, be used with any linearizable neuron model and allows different coding schemes of spike train patterns. The algor...

Learning and Variability in Spiking Neural Networks

Neural networks exhibit ongoing, spatiotemporal patterns of spiking activity. Evidence shows that these patterns are metastable, i.e. temporary, transient, and non-stationary. Metastability is theorized to be adaptive for neural and cognitive function, but learning must somehow remain stable in the context of highly variable spike dynamics. In the present study, a neural network learning algori...

Supervised learning in spiking neural networks