KIP publications

year 2023
author(s) Jakob Kaiser, Raphael Stock, Eric Müller, Johannes Schemmel, Sebastian Schmitt
title Simulation-based inference for model parameterization on analog neuromorphic hardware
KIP-Nummer HD-KIP 23-84
KIP-Gruppe(n) F9
document type Paper
Keywords (shown) analog, neuromorphic, simulation-based inference, multi-compartment
source Jakob Kaiser et al 2023 Neuromorph. Comput. Eng. 3 044006
doi 10.1088/2634-4386/ad046d
Abstract (en)

The BrainScaleS-2 (BSS-2) system implements physical models of neurons as well as synapses and aims for an energy-efficient and fast emulation of biological neurons. When replicating neuroscientific experiments on BSS-2, a major challenge is finding suitable model parameters. This study investigates the suitability of the sequential neural posterior estimation (SNPE) algorithm for parameterizing a multi-compartmental neuron model emulated on the BSS-2 analog neuromorphic system. The SNPE algorithm belongs to the class of simulation-based inference methods and estimates the posterior distribution of the model parameters; access to the posterior allows quantifying the confidence in parameter estimations and unveiling correlation between model parameters. For our multi-compartmental model, we show that the approximated posterior agrees with experimental observations and that the identified correlation between parameters fits theoretical expectations. Furthermore, as already shown for software simulations, the algorithm can deal with high-dimensional observations and parameter spaces when the data is generated by emulations on BSS-2. These results suggest that the SNPE algorithm is a promising approach for automating the parameterization and the analyzation of complex models, especially when dealing with characteristic properties of analog neuromorphic substrates, such as trial-to-trial variations or limited parameter ranges.

  author   = {Kaiser, Jakob and Stock, Raphael and M{\"u}ller, Eric and Schemmel, Johannes and Schmitt, Sebastian},
  title    = {Simulation-based inference for model parameterization on analog neuromorphic hardware},
  journal  = {Neuromorphic Computing and Engineering},
  year     = {2023},
  volume   = {3},
  number   = {4},
  pages    = {044006},
  doi      = {10.1088/2634-4386/ad046d}
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