English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The NEURON simulator has been developed over the past three decades and iswidely used by neuroscientists to model the electrical activity of neuronalnetworks. Large network simulation projects using NEURON have supercomputerallocations that individually measure in the millions of core hours.Supercomputer centers are transitioning to next generation architectures andthe work accomplished per core hour for these simulations could be improved byan order of magnitude if NEURON was able to better utilize those new hardwarecapabilities. In order to adapt NEURON to evolving computer architectures, thecompute engine of the NEURON simulator has been extracted and has beenoptimized as a library called CoreNEURON. This paper presents the design,implementation and optimizations of CoreNEURON. We describe how CoreNEURON canbe used as a library with NEURON and then compare performance of differentnetwork models on multiple architectures including IBM BlueGene/Q, IntelSkylake, Intel MIC and NVIDIA GPU. We show how CoreNEURON can simulate existingNEURON network models with 4-7x less memory usage and 2-7x less execution timewhile maintaining binary result compatibility with NEURON.

CoreNEURON : An Optimized Compute Engine for the NEURON Simulator