Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate

12/30/2019
by   Sebastian Billaudelle, et al.
22

We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control. The high acceleration factor of 1000 compared to biological dynamics enables the execution of computationally expensive tasks, by allowing the fast emulation of long-duration experiments or rapid iteration over many consecutive trials. The flexibility of our architecture is demonstrated in a suite of five distinct experiments, which emphasize different aspects of the BrainScaleS-2 system.

READ FULL TEXT
research
05/28/2019

Harnessing Slow Dynamics in Neuromorphic Computation

Neuromorphic Computing is a nascent research field in which models and d...
research
05/23/2022

Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity

Memory is a key component of biological neural systems that enables the ...
research
01/26/2022

The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity

Since the beginning of information processing by electronic components, ...
research
09/24/2019

Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network

Future developments in artificial intelligence will profit from the exis...
research
03/30/2020

Extending BrainScaleS OS for BrainScaleS-2

BrainScaleS-2 is a mixed-signal accelerated neuromorphic system targeted...
research
05/04/2023

SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing

In many neuromorphic workflows, simulators play a vital role for importa...
research
12/27/2019

Structural plasticity on an accelerated analog neuromorphic hardware system

In computational neuroscience, as well as in machine learning, neuromorp...

Please sign up or login with your details

Forgot password? Click here to reset