Mitigating Computer Limitations in Replicating Numerical Simulations of a Neural Network Model With Hodgkin-Huxley-Type Neurons.

ABSTRACT: Computational experiments have been very important to numerically simulate real phenomena in several areas. Many studies in computational biology discuss the necessity to obtain numerical replicability to accomplish new investigations. However, even following well-established rules in the literature, numerical replicability is unsuccessful when it takes the computer's limitations for representing real numbers into consideration. In this study, we used a previous published recurrent network model composed by Hodgkin-Huxley-type neurons to simulate the neural activity during development. The original source code in C/C++ was carefully refactored to mitigate the lack of replicability; moreover, it was re-implemented to other programming languages/software (XPP/XPPAUT, Python and Matlab) and e

SUBMITTER: Lopes PH 

PROVIDER: S-EPMC9135410 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature