Published December 16, 2020 | Version v1
Conference paper Open

A novel simulator for extended Hodgkin-Huxley neural networks

  • 1. MicroLab, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
  • 2. Erasmus Medical Center, Rotterdam, Netherlands


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.



Files (258.2 kB)

Name Size Download all
258.2 kB Preview Download

Additional details


EXA2PRO – Enhancing Programmability and boosting Performance Portability for Exascale Computing Systems 801015
European Commission