Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware

TL;DR

This paper demonstrates that simulation-based inference, specifically the SNPE algorithm, effectively estimates parameters of complex neuron models on analog neuromorphic hardware, aligning with experimental data and theoretical expectations.

Contribution

It shows that SNPE can be used for automated parameterization of neuromorphic models on hardware, handling high-dimensional data and capturing parameter correlations.

Findings

SNPE estimates align with experimental observations.

The algorithm captures theoretical parameter correlations.

SNPE handles high-dimensional data on neuromorphic hardware.

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.