Simulation-based inference for model parameterization on analog neuromorphic hardware [data]
This data is presented in the paper "Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware". The abstract reads as follows: The BrainScaleS-2 (BSS-2) system implements physical models of neurons as well as synapses and aims for an energy-efficient and fast e...
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| Main Authors: | , , , , |
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| Format: | Database Research Data |
| Language: | English |
| Published: |
Heidelberg
Universität
2023-09-28
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| DOI: | 10.11588/data/AVFF2E |
| Subjects: | |
| Online Access: | Resolving-System, kostenfrei, Volltext: https://doi.org/10.11588/data/AVFF2E Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/AVFF2E 
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| Author Notes: | Jakob Kaiser, Raphael Stock, Eric Müller, Johannes Schemmel, Sebastian Schmitt |
MARC
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| 520 | |a This data is presented in the paper "Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware". The abstract reads as follows: 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. In contrast to other optimization methods such as genetic algorithms or stochastic searches, the SNPE algorithms belongs to the class of simulation-based inference (SBI) 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. | ||
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