Dr Johanna Senk

Computational neuroscience; spiking neural networks

We develop and simulate computational models to study the structure, dynamics, and function of neuronal networks in the brain. Our focus is on large networks of individually represented spiking neurons, which allow us to ask how experimental evidence on neuron types and connectivity relates to the network activity we can observe. Beyond static connectivity, we are interested in how networks change through synaptic and structural plasticity, as a route to understanding the origins of learning and memory in natural intelligence.

We complement our simulations with mean-field theory, which provides analytical insight into the biological mechanisms. We are also involved in developing simulation technology for high-performance computing systems, and in benchmarking its performance against neuromorphic hardware. Throughout, we are committed to reproducible science: unambiguous model descriptions, openly available code, and models that the community can reuse and extend.

We are part of several (international) collaborations, and we welcome further collaboration and co-supervision across Neuroscience and Informatics.

Visit Dr Johanna Senk's profile for more information and a full publication list, or contact her directly at J.Senk@sussex.ac.uk for informal enquires. PhD projects can lean towards neuroscience, theory, or computing, and we welcome students from backgrounds such as neuroscience, physics, mathematics, and computer science.

Key references

• B. Golosio, G. Tiddia, J. Villamar, L. Pontisso, L. Sergi, F. Simula, P. Babu, E. Pastorelli, A. Morrison, M. Diesmann, A. Lonardo, P. S. Paolucci, J. Senk (2026) Scalable construction of spiking neural networks using up to thousands of GPUs. Neuromorph. Comput. Eng. 6(2): 024012. doi: https://doi.org/10.1088/2634-4386/ae65d2
• J. C. Knight, J. Senk, T. Nowotny (2026) A flexible framework for structural plasticity in GPU-accelerated sparse spiking neural networks. Neuromorph. Comput. Eng. 6(1): 014019. doi: https://doi.org/10.1088/2634-4386/ae4535
• J. Senk, A. C. Kurth, S. Furber, et al. (2026) Constructive community race: full-density spiking neural network model drives neuromorphic computing. Neuromorph. Comput. Eng. 6(1): 012001. doi: https://doi.org/10.1088/2634-4386/ae379a
• J. Senk, E. Hagen, S. J. van Albada, M. Diesmann (2024) Reconciliation of weak pairwise spike–train correlations and highly coherent local field potentials across space. Cereb. Cortex 34(10): bhae405. doi: https://doi.org/10.1093/cercor/bhae405
• J. Senk, B. Kriener, M. Djurfeldt, N. Voges, H.-J. Jiang, L. Schüttler, G. Gramelsberger, M. Diesmann, H. E. Plesser, S. J. van Albada (2022) Connectivity concepts in neuronal network modeling. PLoS Comput. Biol. 18(9): e1010086. doi: https://doi.org/10.1371/journal.pcbi.1010086
• M. Layer, J. Senk, S. Essink, A. van Meegen, H. Bos, M. Helias (2022) NNMT: Mean-field based analysis tools for neuronal network models. Front. Neuroinform. 16: 835657. doi: https://doi.org/10.3389/fninf.2022.835657
• J. Senk, K. Korvasová, J. Schuecker, E. Hagen, T. Tetzlaff, M. Diesmann, M. Helias (2020) Conditions for wave trains in spiking neural networks. Phys. Rev. Research 2: 023174. doi: https://doi.org/10.1103/PhysRevResearch.2.023174