Thesis Project Form

Recently, novel approaches to perceptual systems have been proposed, which combine retina-like vision sensors with brain-inspired spiking neural processors to build sophisticated real-time event-based early visual processing systems. These networks operate using exclusively precisely-timed temporal contrast events. Conversely, the vast majority of computational neuroscience models are based on mean firing rates of spatial contrast and do not rely on the precise timing of spikes.

Design efficient distributed architectures that model populations of neurons characterized by Gabor-like spatio-temporal receptive fields on which to base local estimates of image motion. To achieve this goal, rate-based computational models should be properly mapped and generalized to spike-based solutions.
The performance of the resulting architecture will be eventually assessed on real-world continuous-time event-based visual data

The student will learn to employ different methodologies and instrumentation, including:
- Modeling of spiking neural networks using the Python Brian neural network simulator
- Acquisition of signals from a silicon retina producing both image frames and streams of spikes in response to temporal contrast changes (DAVIS silicon retina sensor)
- Programming convolutional networks on GPUs