Parallel Computing ePosters

The generation of appropriate parameter configurations for high-dimensional hierarchical spiking neural network models is currently one of the slowest and most frustrating tasks in the computational neuroscience workflow. Moreover, many of the currently used methods for multidimensional parametrization only provide…

Background: Biophysical network modeling (BNM) of the brain is a promising technique for bridging macro- and microscale levels of investigation, enabling inferences about latent features of brain activity such as excitation-inhibition balance. This approach allows personalized models of the brain to be fitted to…

Biophysical neuron models provide mechanistic insight about empirically observed phenomena. However, biophysical neuron models are expensive to simulate, thereby limiting the scale of biophysical models, and optimizing their parameters is notoriously difficult, thereby preventing the fitting of biophysical models to…

Since the popularization of GPUs for machine learning (ML) workloads, several dedicated accelerator chips have emerged, offering architectures optimized for common ML operations and requirements. The types of tasks targeted by such platforms however - often sparse and highly parallel - are not confined to the realm of…

The domain of computational neuroscience is one that is awash with data; be it from simulations or experiments, we try to extract patterns and make sense of them. However, two trends have emerged over the past decade. For one, datasets are rapidly increasing in scale, breadth and depth, as more research demands higher…

The Brian simulator, a simulator for spiking neural networks, started its life in 2007 and has been continuously developed ever since. A complete rewrite in 2014 led to the "brian2" package, which is now built as a code generation framework. This means that users write their code as a Python script, with textual…