BibTeX

@article{2303.16056v2,
Author        = {Jakob Kaiser and Raphael Stock and Eric Müller and Johannes Schemmel and Sebastian Schmitt},
Title         = {Simulation-based Inference for Model Parameterization on Analog
  Neuromorphic Hardware},
Eprint        = {2303.16056v2},
DOI           = {10.1088/2634-4386/ad046d},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.NE},
Abstract      = {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.},
Year          = {2023},
Month         = {Mar},
Note          = {Neuromorph. Comput. Eng. 3 044006 (2023)},
Url           = {http://arxiv.org/abs/2303.16056v2},
File          = {2303.16056v2.pdf}
}