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Parametrizing analog multi-compartment neurons with genetic algorithms.

Raphael Stock¹, Jakob Kaiser¹, Eric Müller²

¹Kirchhoff Institute for Physics, Heidelberg University, Heidelberg, 69120, Germany.

Open Research Europe

|December 13, 2024

Summary

This summary is machine-generated.

Genetic algorithms effectively parameterize analog neuromorphic hardware by replicating excitatory postsynaptic potential (EPSP) attenuation. This method bypasses the need for domain-specific knowledge, simplifying neuron model calibration.

Keywords:

analog computing genetic algorithm multi-compartment neuromorphic

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Area of Science:

Computational Neuroscience
Neuromorphic Engineering

Background:

Parameterizing multi-compartmental neuron models is complex, as key values like leak and axial conductance are hard to derive directly from observations.
Accurate parameterization is vital for models to replicate biological neuron behavior, such as signal propagation.

Purpose of the Study:

To replicate the attenuation of an excitatory postsynaptic potential (EPSP) along a linear chain of compartments.
To test the efficacy of genetic algorithms for parameterizing analog neuromorphic hardware, specifically the BrainScaleS-2 platform.

Main Methods:

Employed genetic algorithms to determine suitable model parameters for neuron simulations.
Validated genetic algorithm results using a comprehensive grid search.
Utilized spike-triggered averaging to mitigate trial-to-trial variations inherent in analog systems.

Main Results:

Genetic algorithms successfully replicated the target EPSP attenuation behavior.
Both single-objective and multi-objective genetic algorithm searches proved effective.
Demonstrated the practical application of genetic algorithms for analog neuromorphic hardware parameterization.

Conclusions:

Genetic algorithms offer a viable approach for parameterizing analog neuromorphic hardware without requiring deep substrate-specific knowledge.
The study validates the use of genetic algorithms for complex tasks like replicating signal propagation dynamics in neuron models.