Professor Thomas Nowotny

Professor of Informatics

Computational neuroscience and bio-inspired AI

In our group we use computational and hybrid systems approaches to better understand the properties and function of sensory and motor systems as well as taking inspiration from the brain to build better AI. This involves three main areas of research:

1. bio-inspired machine learning and computational neuroscience models of (mainly olfactory) systems and,
2. using GPU technology to accelerate the simulation of Spiking Neural Networks
3. hybrid systems research for active closed-loop probing of neural circuits.

I am happy to supervise a PhD in any of these three areas. Work on olfaction could include continuing the work on models of odour sensing in the peripheral olfactory system of insects. For the computationally minded, large scale simulations of olfactory systems and/or networks for AI applications inspired by insect brains with GeNN are an option.

Collaborations within Sussex Neuroscience: We have a long-standing collaboration with George & Ildiko Kemenes, Kevin Staras and Paul Graham and are also now working with Claudio Alonso.

Key references

J. C. Knight and T. Nowotny (2021) Larger GPU-accelerated brain simulations with procedural connectivity. Nat Comput Sci 1(2): 136-42. doi: 10.1038/s43588-020-00022-7, free full text
James E. M. Bennett, Andrew Philippides, Thomas Nowotny, Learning with reinforcement prediction errors in a model of the Drosophila mushroom body. Nature Communications 12: 2569. doi: 10.1038/s41467-021-22592-4
H. K. Chan, F. Hersperger, E. Marachlian, B. H. Smith, F. Locatelli, P. Szyszka, T. Nowotny (2018) Odorant mixtures elicit less variable and faster responses than pure odorants. PLoS Comp Biol 14(12):e1006536. doi: 10.1007/s00422-019-00797-7
J. C. Knight, T. Nowotny (2018) GPUs outperform current HPC and neuromorphic solutions in terms of speed and energy when simulating a highly-connected cortical model. Front Neurosci 12:941. doi: 10.3389/fnins.2018.00941
E. Yavuz, J. Turner and T. Nowotny (2016). GeNN: a code generation framework for accelerated brain simulations. Scientific Reports 6:18854. doi: 10.1038/srep18854
T. Nowotny, R. Huerta, H. D. I. Abarbanel, and M. I. Rabinovich Self-organization in the olfactory system: One shot odor recognition in insects, Biol Cyber, 93 (6): 436-446 (2005), DOI:10.1007/s00422-005-0019-7.

For a full list of publications and more details about the lab, visit Professor Thomas Nowotny's profile.