Work

A Pipeline based on Differential Evolution for Tuning Parameters of Synaptic Dynamics Models

Year: 2025

Type: conference-paper

Abstract: Integrating the modulatory properties of Synaptic Dynamics (SD) into Spiking Neural Networks (SNNs) can enhance their computational capabilities.For improving this integration process, this paper presents a pipeline based on Differential Evolution to tune parameters of SD models.Using reference signals from in vitro experiments, parameters of two models are tuned as study cases: the Tsodyks-Markram and the Modified Stochastic Synaptic Model.The pipeline has an average success rate of 75% and 80% respectively.The outcome is a distribution of parameters for each model, which can be considered as prior knowledge to facilitate the integration of SD models into SNNs.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-111

Authors: Ferney Beltran-Velandia, Nico Scherf, Martin Bogdan

Institutions: Max Planck Institute for Human Cognitive and Brain Sciences, Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: bronze

Funder: Bundesministerium für Bildung und Forschung

Award: G3740268834