Moritz Milde

Position:
PhD Student
Email:
Work phone:
+41 44 6353046
Location:

I received my B.Sc. degree in biomimetics at the Westphalian University of Applied Science Bocholt in 2015. This line of studies focused on mechanical engineering with specification in light weight construction, as well as biology with specialization in sensory systems and bio-inspired information processing.
I deepened my knowledge in biological sensory information processing during my M.Sc. in Neurobiology at Bielefeld University. I worked together with Martin Egelhaaf and Elisabetta Chicca on the implementation of the visual information processing system of flying insects onto neuromorphic hardware and applied the abstracted artificial information processing network in the context of driving robots in order to avoid collisions with surrounding objects.

I am interested into transferring insights of biological information processing onto state-of-the-art soft- and hardware. The transfer is not a one-to-one copy of the biological model, but rather an abstraction of the underlying principle in order to implement the developed neural circuits in Spiking Neural Networks (SNN).


Currently I am developing a Driving Assistance System (DAS), which uses Convolutional Neural Networks (CNN) to extract features from recorded frames. The extracted information is further used to localize and classify task-relevant objects, such as cars or pedestrians. The spatial-temporal change of objects and the change in their respective spatial relation between successive frames, is used to understand the temporal evolution of a driving scene.
In parallel I am exploring SNN architectures, which directly operate on local spatio-temporal contrast changes (events) rather than frames. These networks inherently represent time in their nature of computation. I am investigating how we can introduce a conceptual understanding of spatio-temporal patterns in the context of driving robotic systems.
Both approaches tackle a similar question but operate within different computation paradigms. However, if we are able represent the dynamic environment not only in its 2D structure, but also take time into account, we can introduce a notion of conceptual understanding in artificial systems.

Publications

2017

2016

2015

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