Timo Oess

PostDoc at Ulm University interested in computational modeling.

About

Currently, I am a postdoctoral researcher with Marc Ernst at the Applied Cognitive Psychology Department at Ulm University where I continue to work on computational models for control, learning and auditory motion perception.

Before that, I was a postdoctoral researcher with Carsten Mehring at the Neurobiology and Neurotechnology Group at the Bernstein Center Freiburg working on computational models for motor control. Specifically, I was using recurrent neural networks and reinforcement learning to construct biologically plausible models of the motor cortex.

Before that, I was a PhD student with Marc Ernst and Heiko Neumann at the Applied Cognitive Psychology Department at Ulm University, from where received a PhD in computer science in 2021. The topic of my PhD thesis was From sound waves to locations : computational models for sound source localization in the early auditory pathway.

The thesis focuses on computational models for auditory sound source localization, multisensory integration and the deployment of such models on neuromorphic hardware and robotic platforms (see project website). To conduct experiments with these models, I designed a 3D-printed movable human head (see video) and a experimental setup for testing human behavior.

In 2016, I graduated from Technical University München (TUM) with a Master’s degree in Robotics, Cognition and Intelligence. For my master’s thesis, I went to the Cognitive Anteater Robotics Laboratory (CARL) at the University of California as a junior specialist, working with Jeffrey Krichmar on a computational model for spatial navigation see Frontiers in Neurorobotics 2017.


Publications

2023

  • Listen to the Brain–Auditory Sound Source Localization in Neuromorphic Computing Architectures, Schmid; Oess ; Neumann, Sensors, Full text

2020

  • Binaural Signal Integration Improves Vertical Sound Source Localization, bioRxiv, Preprint Full Text
  • Computational principles of neural adaptation for binaural signal integration, PLOS Computational Biology, Full text
  • From near-optimal Bayesian Integration to Neuromorphic Hardware: A neural network model of multisensory integration, Frontiers in Neurorobotics, Full text
  • A Bio-Inspired Model of Sound Source Localization on Neuromorphic Hardware, International Conference on Artificial Intelligence Circuits and Systems (AICAS), Full text
  • Computational investigation of visually guided learning of spatially aligned auditory maps in the colliculus, Proceedings of the International Symposium on Auditory and Audiological Research, Full text

2017

  • A Computational Model for Spatial Navigation Based on Reference Frames in the Hippocampus, Retrosplenial Cortex, and Posterior Parietal Cortex, Frontiers in Neurorobotics, Full text


News

Presentations


Videos

Auditory Cue Following