Computational-neuroscience simulators have traditionally been constrained by tightly coupled simulation engines and modeling languages, limiting their flexibility and scalability. Retrofitting these platforms to accommodate new backends is often costly, and sharing models across simulators remains cumbersome. This paper puts forward an alternative approach based on the EDEN neural simulator, which introduces a modular stack that decouples abstract model descriptions from execution. This architecture enhances flexibility and extensibility by enabling seamless integration of multiple backends, including hardware accelerators, without extensive reprogramming. Through the use of NeuroML, simulation developers can focus on high-performance execution, while model users benefit from improved portability without the need to implement custom simulation engines. Additionally, the proposed method for incorporating arbitrary simulation platforms—from model-optimized code kernels to custom hardware devices—as backends offers a more sustainable and adaptable framework for the computational-neuroscience community. The effectiveness of EDEN's approach is demonstrated by integrating two distinct backends: flexHH, an FPGA-based accelerator for extended Hodgkin-Huxley networks, and SpiNNaker, the well-known, neuromorphic platform for large-scale spiking neural networks. Experimental results show that EDEN integrates the different backends with minimal effort while maintaining competitive performance, reaffirming it as a robust, extensible platform that advances the design paradigm for neural simulators by achieving high generality, performance, and usability.

Modern neuroscience employs in silico experimentation on ever-increasing and more detailed neural networks. The high modeling detail goes hand in hand with the need for high model reproducibility, reusability and transparency. Besides, the size of the models and the long timescales under study mandate the use of a simulation system with high computational performance, so as to provide an acceptable time to result. In this work, we present EDEN (Extensible Dynamics Engine for Networks), a new general-purpose, NeuroML-based neural simulator that achieves both high model flexibility and high computational performance, through an innovative model-analysis and code-generation technique. The simulator runs NeuroML-v2 models directly, eliminating the need for users to learn yet another simulator-specific, model-specification language. EDEN's functional correctness and computational performance were assessed through NeuroML models available on the NeuroML-DB and Open Source Brain model repositories. In qualitative experiments, the results produced by EDEN were verified against the established NEURON simulator, for a wide range of models. At the same time, computational-performance benchmarks reveal that EDEN runs from one to nearly two orders-of-magnitude faster than NEURON on a typical desktop computer, and does so without additional effort from the user. Finally, and without added user effort, EDEN has been built from scratch to scale seamlessly over multiple CPUs and across computer clusters, when available.

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.