Parametrizing analog multi-compartment neurons with genetic algorithms

Raphael Stock; Jakob Kaiser; Eric Müller; Johannes Schemmel; Sebastian Schmitt

doi:10.12688/openreseurope.15775.2

Parametrizing analog multi-compartment neurons with genetic algorithms

Open Res Eur. 2024 Nov 14:3:144. doi: 10.12688/openreseurope.15775.2. eCollection 2023.

Authors

Raphael Stock¹, Jakob Kaiser¹, Eric Müller², Johannes Schemmel¹, Sebastian Schmitt³

Affiliations

¹ Kirchhoff Institute for Physics, Heidelberg University, Heidelberg, 69120, Germany.
² European Institute for Neuromorphic Computing, Heidelberg University, Heidelberg, 69120, Germany.
³ Department for Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, 37077, Germany.

Abstract

Background: Finding appropriate model parameters for multi-compartmental neuron models can be challenging. Parameters such as the leak and axial conductance are not always directly derivable from neuron observations but are crucial for replicating desired observations. The objective of this study is to replicate the attenuation behavior of an excitatory postsynaptic potential (EPSP) traveling along a linear chain of compartments on the analog BrainScaleS-2 neuromorphic hardware platform.

Methods: In the present publication we use genetic algorithms to find suitable model parameters. They promise parameterization without domain knowledge of the neuromorphic substrate or underlying neuron model. To validate the results of the genetic algorithms, a comprehensive grid search was conducted. Furthermore, trial-to-trial variations in the analog system are counteracted utilizing spike-triggered averaging.

Results and conclusions: The algorithm successfully replicated the desired EPSP attenuation behavior in both single and multi-objective searches illustrating the applicability of genetic algorithms to parameterize analog neuromorphic hardware.

Keywords: analog computing; genetic algorithm; multi-compartment; neuromorphic.

Grants and funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 720270 (Human Brain Project Specific Grant Agreement 1 [HBP SGA1]), grant agreement No 785907 (Human Brain Project Specific Grant Agreement 2 [HBP SGA2]) and grant agreement number 945539 (Human Brain Project Specific Grant Agreement 3 [HBP SGA3]). As well as funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy EXC 2181/1-390900948 (the Heidelberg STRUCTURES Excellence Cluster) as well as from the Manfred St\"ark Foundation. This work has received funding from the EU ([FP7/2007–2013], [H2020/2014–2020]) under grant agreements 604102 (HBP).