Neuromorphic Algorithms for Modeling Visual Perception

How machines can see and react the way brains do — neuromorphic sensing, event-based vision, and spiking neural networks, from theory to hands-on robotics.

SemesterSpring 2027
CreditsTBD
FormatLectures + labs
LevelMSc / advanced BSc

You just played a (very simplified) event camera and spiking neuron. Read on to see how the real thing works.

Overview

This course introduces neuromorphic sensing and computing: event-based cameras that report per-pixel brightness changes instead of full frames, and spiking neural networks that process that information the way biological neurons do. Students will learn the theory behind both and build small working systems on real neuromorphic hardware.

What you'll cover

  • How event cameras work, and why they differ fundamentally from frame-based cameras
  • Spiking neuron models: leaky integrate-and-fire, refractory periods, membrane dynamics
  • Training and deploying spiking neural networks
  • Neuromorphic hardware platforms (e.g. SpiNNaker)
  • Applications in robotics: fast, low-power, event-driven perception

Prerequisites

Basic programming (Python), and familiarity with linear algebra and signals. No prior neuroscience background required.

Interested?

Get in touch via the contact page, or reach out directly to discuss enrollment, prerequisites, or project ideas.