Structural plasticity on an accelerated analog neuromorphic hardware system.

Clicks: 202
ID: 204873
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #38 of 55 articles by views in neural networks : the official journal of the international neural network society

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their neural connectivity and synaptic capacity depend on their specific design choices, but is always intrinsically limited. Here, we present a strategy to achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- and postsynaptic partners while keeping the neuronal fan-in constant and the connectome sparse. In particular, we implemented this algorithm on the analog neuromorphic system BrainScaleS-2. It was executed on a custom embedded digital processor located on chip, accompanying the mixed-signal substrate of spiking neurons and synapse circuits. We evaluated our implementation in a simple supervised learning scenario, showing its ability to optimize the network topology with respect to the nature of its training data, as well as its overall computational efficiency.
Reference Key
billaudelle2020structuralneural Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Billaudelle, Sebastian;Cramer, Benjamin;Petrovici, Mihai A;Schreiber, Korbinian;Kappel, David;Schemmel, Johannes;Meier, Karlheinz;
Journal neural networks : the official journal of the international neural network society
Year 2020
DOI
S0893-6080(20)30355-5
URL
Keywords
neural networks brainscales receptive fields spiking structural plasticity

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.