Imagine you’re baking a complex cake with multiple layers, frostings, and decorations. But instead of having all the ingredients and tools readily available, you need to search through different stores, figure out the quantities needed, and keep track of everything yourself. It would be quite a daunting task! Similarly, in the field of computational neuroscience, setting up large-scale brain simulations has become increasingly challenging due to the complexity of models and software dependencies. However, researchers have developed a solution called Neural Simulation Pipeline (NSP) that simplifies this process. NSP acts as an infrastructure that streamlines the setup and execution of brain simulations, making them more accessible and cost-effective. It utilizes a containerization approach, similar to how you might use storage containers to organize your baking supplies. By packaging simulations into container-based environments, NSP enables researchers to deploy simulations on-premise or in public clouds with ease. This means they can take advantage of the flexibility and cost savings offered by cloud services like Amazon Web Services (AWS). With NSP, scientists can focus on their research rather than getting tangled in the logistics of simulation setup. So if you’re curious about exploring the exciting world of brain simulations, dive into the research behind Neural Simulation Pipeline!

In this study, we explore the simulation setup in computational neuroscience. We use GENESIS, a general purpose simulation engine for sub-cellular components and biochemical reactions, realistic neuron models, large neural networks, and system-level models. GENESIS supports developing and running computer simulations but leaves a gap for setting up today’s larger and more complex models. The field of realistic models of brain networks has overgrown the simplicity of earliest models. The challenges include managing the complexity of software dependencies and various models, setting up model parameter values, storing the input parameters alongside the results, and providing execution statistics. Moreover, in the high performance computing (HPC) context, public cloud resources are becoming an alternative to the expensive on-premises clusters. We present Neural Simulation Pipeline (NSP), which facilitates the large-scale computer simulations and their deployment to multiple computing infrastructures using the infrastructure as the code (IaC) containerization approach. The authors demonstrate the effectiveness of NSP in a pattern recognition task programmed with GENESIS, through a custom-built visual system, called RetNet(8 × 5,1) that uses biologically plausible Hodgkin–Huxley spiking neurons. We evaluate the pipeline by performing 54 simulations executed on-premise, at the Hasso Plattner Institute’s (HPI) Future Service-Oriented Computing (SOC) Lab, and through the Amazon Web Services (AWS), the biggest public cloud service provider in the world. We report on the non-containerized and containerized execution with Docker, as well as present the cost per simulation in AWS. The results show that our neural simulation pipeline can reduce entry barriers to neural simulations, making them more practical and cost-effective.

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