KIP publications

year 2019
author(s) Sebastian Billaudelle* and Benjamin Cramer* and Mihai A. Petrovici and Korbinian Schreiber and David Kappel and Johannes Schemmel^ and Karlheinz Meier^
title Structural plasticity on an accelerated analog neuromorphic hardware system
KIP-Nummer HD-KIP 19-118
KIP-Gruppe(n) F9
document type Paper
Keywords (shown) Structural plasticity, Receptive fields, BrainScaleS, Spiking, Neural networks
source Neural Networks (2021) 11-20
Abstract (en)

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.

  author   = {Billaudelle, Sebastian and Cramer, Benjamin and Petrovici, Mihai A and Schreiber, Korbinian and Kappel, David and Schemmel, Johannes and Meier, Karlheinz},
  title    = {Structural plasticity on an accelerated analog neuromorphic hardware system},
  journal  = {Neural Networks},
  year     = {2021},
  volume   = {133},
  pages    = {11 - 20},
  note     = {0893-6080},
  doi      = {},
  url      = {}
URL Neural Networks
Datei pdf
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