Parametrizing Analog Multi-Compartment Neurons with Genetic Algorithms [Data] (doi:10.11588/data/U2U1IB)

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Document Description

Citation

Title:

Parametrizing Analog Multi-Compartment Neurons with Genetic Algorithms [Data]

Identification Number:

doi:10.11588/data/U2U1IB

Distributor:

heiDATA

Date of Distribution:

2023-04-18

Version:

1

Bibliographic Citation:

Stock, Raphael; Kaiser, Jakob; Müller, Eric; Schemmel, Johannes; Schmitt, Sebastian, 2023, "Parametrizing Analog Multi-Compartment Neurons with Genetic Algorithms [Data]", https://doi.org/10.11588/data/U2U1IB, heiDATA, V1

Study Description

Citation

Title:

Parametrizing Analog Multi-Compartment Neurons with Genetic Algorithms [Data]

Identification Number:

doi:10.11588/data/U2U1IB

Authoring Entity:

Stock, Raphael (Heidelberg University, Kirchhoff Institute for Physics)

Kaiser, Jakob (Heidelberg University, Kirchhoff Institute for Physics)

Müller, Eric (Heidelberg University, European Institute for Neuromorphic Computing)

Schemmel, Johannes (Heidelberg University, Kirchhoff Institute for Physics)

Schmitt, Sebastian (University Medical Center Göttingen, Department for Neuro- and Sensory Physiology)

Distributor:

heiDATA

Access Authority:

Kaiser, Jakob

Access Authority:

Stock, Raphael

Date of Distribution:

2023-03-17

Holdings Information:

https://doi.org/10.11588/data/U2U1IB

Study Scope

Keywords:

Physics, analog computing, neuromorphic, multi-compartment

Abstract:

This data is presented in the paper: "Parametrizing Analog Multi-Compartment Neurons with Genetic Algorithms" which is currently under review. Further information about the contents of the files can be found in the `README.md`. Abstract: This paper employs genetic algorithms to parameterize the leak conductance and inter-compartment conductance of multi-compartment neurons on the analog BrainScaleS-2 neuromorphic hardware platform. These parameters are not always directly derivable from neuron observations but are crucial for replicating desired observations. Genetic algorithms promise parameterization without domain knowledge of the neuromorphic substrate or underlying neuron model. The objective of this study is to replicate the attenuation behavior of an excitatory postsynaptic potential (EPSP) traveling along a linear chain of compartments, which was observed to exhibit an exponential decay of the EPSP’s amplitude. A comprehensive grid search was conducted to evaluate the solutions from the genetic algorithm. To counteract trial-to-trial variations in analog systems, spike-triggered averaging was utilized. The study demonstrated the multi-objective search capabilities of genetic algorithms, allowing for the constraint of multiple parameters to reach multiple target observables. 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.

Methodology and Processing

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Data Access

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Related Publications

Citation

Title:

Publication under review

Bibliographic Citation:

Publication under review

Other Study-Related Materials

Label:

data.zip

Notes:

application/zip

Other Study-Related Materials

Label:

README.md

Notes:

text/markdown