Computational neuroscience aims to investigate and explain the behaviour and functions of neural structures, through mathematical models. Due to the models' complexity, they can only be explored through computer simulation. Modern research in this field is increasingly adopting large networks of neurons, and diverse, physiologically-detailed neuron models, based on the extended Hodgkin-Huxley (eHH) formalism. However, existing eHH simulators either support highly specific neuron models, or they provide low computational performance, making model exploration costly in time and effort. This work introduces a simulator for extended Hodgkin-Huxley neural networks, on multiprocessing platforms. This simulator supports a broad range of neuron models, while still providing high performance. Simulator performance is evaluated against varying neuron complexity parameters, network size and density, and thread-level parallelism. Results indicate performance is within existing literature for single-model eHH codes, and scales well for large CPU core counts. Ultimately, this application combines model flexibility with high performance, and can serve as a new tool in computational neuroscience.

Biological neural networks, electrophysiology, extended Hodgkin-Huxley neuron model, gap junctions, in silico medicine, OpenMP, parallel processing,
20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020
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Department of Neuroscience

Panagiotou, S. (Sotirios), Miedema, R. (Rene), Sidiropoulos, H. (Harry), Smaragdos, G, Strydis, C, & Soudris, D. (2020). A novel simulator for extended Hodgkin-Huxley neural networks. In Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020 (pp. 395–402). doi:10.1109/BIBE50027.2020.00071