Constructive community race: full-density spiking neural network model drives neuromorphic computing

TL;DR

This paper reviews the development of a full-density spiking neural network model inspired by mammalian brain circuitry, highlighting its impact on neuromorphic computing and benchmarking.

Contribution

It provides an overview of how the model was optimized for real-time performance and reduced energy use, guiding future benchmarks and scientific applications.

Findings

Achieved real-time performance in neuromorphic simulations

Reduced energy consumption significantly

Established a standard benchmark for brain-inspired models

Abstract

The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a model was proposed representing all neurons and synapses of the stereotypical cortical microcircuit below $1\,\text{mm}^2$ of brain surface. The model reproduces fundamental features of brain activity but its impact remained limited because of its computational demands. For theory and simulation, however, the model was a breakthrough because it removes uncertainties of downscaling, and larger models are less densely connected. This sparked a race in the neuromorphic computing community and the model became a de facto standard benchmark. Within a few years real-time performance was reached and surpassed at significantly reduced energy consumption. We review how the computational challenge was tackled by different simulation technologies and derive guidelines for the next generation of benchmarks and other domains of science.