Sussex Neuroscience

Professor Thomas Nowotny

ThomasComputational neuroscience

In our group we use computational and hybrid systems approaches to better understand the properties and function of sensory and motor systems. This involves three main areas of research: (i) computational neuroscience models of olfactory systems and bio-inspired machine learning, (ii) using GPU technology to accelerate the simulation of brain circuits and (iii) hybrid systems research for active closed-loop probing of neural circuits.
I am happy to supervise a lab rotation and 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 [1-2]. For the computationally minded, large scale simulations of olfactory systems and/or networks for AI applications inspired by insect brains with GeNN [3-5] are an option. Finally, in hybrid systems research we are working on a methodology to build faithful conductance-based models of individual cells in the lab without the use of chemical blockers. This work would involve both programming and electrophysiology.

Collaborations within SN: We have a long-standing collaboration with George & Ildiko Kemenes, Kevin Staras and Paul Graham and are also now starting to work with Claudio Alonso.

Most relevant publications
(For full list of publications and more details about the lab, visit: http://www.sussex.ac.uk/Users/tn41/)

1. 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
2. T. Nowotny, Jacob S. Stierle, C. Giovanni Galizia, Paul Szyszka, Data-driven honeybee antennal lobe model suggests how stimulus-onset asynchrony can aid odour segregation, Brain Research, 1536: 119-134 (2013) DOI: 10.1016/j.brainres.2013.05.038
3. 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
4. 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
5. 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