Work

Predicting Neuronal Resting Potential Using LSTM Networks

Year: 2025

Type: article

Abstract: I developed a computational model to simulate the resting potential of neurons, aiming to predict membrane voltage dynamics based on physiological parameters. I implemented and evaluated various neural network architectures, systematically exploring the impact of hidden dimensions, number of layers, learning rates, and regularization strategies. The results demonstrate that the models can accurately predict neuronal resting potentials, with strong agreement between predicted and observed values, providing a robust framework for understanding and modeling neuronal behavior.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17211059

Author: Amaro, Guilherme

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W7083460348

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