Quantitative lunch -- Christof Fehrman, Introduction to Spiking Neural Networks.

Spiking neural networks (SNNs) are an artificial neural network architecture that has gained popularity in recent years. These networks are more biologically plausible than traditional network architectures in that each unit produces a temporally precise ‘spike’, similar to the generation of action potentials in the nervous system. This talk will provide an introduction in how to construct and analyze SNNs as well as their use as a research tool to both neuroscientists and quantitative researchers. Specific attention will be given to applications in computational experimentation, simulating neural activity, and tackling traditional problems in machine learning. Additionally, some basic results will be shown using the Python package Brian 2, which can simulate networks of neurons at many different levels of complexity.