Estimating currents from action potentials using single and multi-output neural network models
Tarih
2022Yazar
Süleymanoğlu, Selim
Şengül Ayan, Sevgi
Özdoğan, Hasan
Üst veri
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Ion channels are water-filled pores formed by membrane proteins abundant in excitable cells' plasma
membranes. Ion channels are responsible for converting signals into biological responses. They are primarily
engaged in the generation of short-term action potentials via the combined activity of particular ionic
currents. The goal of this study is to provide a neural network model that was used to predict 13 ionic currents
(such as sodium and calcium channels) from different action potential (AP) shapes. We use a single-cell model
to perform electrophysiological simulations and obtain AP and 13 current shapes based on variations in the
ion channel conductance in cardiomyocytes, which we then compare to experimental results. Constantly
increasing and decreasing the conductance of each ion channel produces 880 different sets of AP shapes and
current shapes, as well as one standard AP shape and 13 standard current shapes without causing any
changes in the conductance of any other ion channel. Next, we calculate the AP difference shapes and feed
them into our neural network along with the passage of time, in order to demonstrate how the dynamics of
action potential induction, movement of the action potential, and the release of neurotransmitters affect the
function of ion channel function. As a starting point for these calculations, the Hodgkin-Huxley model is
utilized. In this study, we demonstrate that using only AP shapes and MATLAB's neural network tool, it is
possible to predict changed ion channel currents with high prediction accuracy.