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. As principal investigator of the Ulm Postdoc Research Grant project From Sound to Attention via Adaptive Perceptual Filtering, I am setting up EEG experiments on auditory attention. To study auditory motion perception during active head and eye movements, we use a cable-driven robot that moves real sound sources around the listener while head and eye movements are tracked (see video).

In summer 2026, I was a visiting researcher in Rob Allison’s lab at the Centre for Vision Research, York University (Toronto), where I investigated the role of eye movements in auditory motion perception using virtual reality and eye tracking (funded by the Ontario–Baden-Württemberg Faculty Mobility Program).

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. After my master’s, I co-founded the start-up Foxim (Cologne), a conversational-commerce platform, where I was lead developer (2016–2017).


Publications

Submitted & Preprints

  • Natural spatial hearing relies on binaural spectral disparity to cancel source-spectrum ambiguities, Oess, Roy, Neumann, Ernst Earlier version on bioRxiv
  • Moving heads, moving sounds: systematic errors in auditory motion perception, Oess, Harris, Ernst, bioRxiv 2026, Preprint Full Text
  • The where and when of tracking sound sources: head-based auditory motion tracking integrates velocity and spatial error within a common delayed frame, Oess, Iriventi, Ernst, bioRxiv 2026, Preprint Full Text
  • Adding 3D space to human navigation: Humans stop short of home after walking over a bridge, Roy, Wünning, Oess, Ernst, PsyArXiv 2026, Preprint Full Text

2026

  • Dynamic sensor adaptation based on efferent feedback for adaptive bio-inspired sound source localization, Durstewitz, Schmid, Oess, Ghazanfari, Neumann, Ernst and Lenk , Frontiers in Neuroscience, Full text

2023

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

2021

  • From sound waves to locations: computational models for sound source localization in the early auditory pathway, Oess, Doctoral dissertation, Ulm University, Full text
  • Brain-inspired Visual-Auditory Integration Yielding Near Optimal Performance – Modelling and Neuromorphic Algorithms, Oess, Neumann, ERCIM News 125, Full text

2020

  • 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

2013

  • Multiobjective Reinforcement Learning Using Actor-Critic Framework and Reservoir Computing, Oubbati, Oess, Fischer, Palm, Workshop on Reinforcement Learning with Generalized Feedback (ECML PKDD), Full text


News

Presentations


Videos

Moving Sound Sources Setup

A cable-driven robot moves a real sound source around the listener, while head movements are tracked, to study auditory motion perception during active head movements.

Auditory Cue Following