A computational approach for solving the Gierer-Meinhardt (G-M) model in the context of biological pattern formation

A mathematical framework with a particular focus on developmental biology can be attained from the Gierer-Meinhardt model, which explains the emergence of spatial patterns within biological systems. These patterns emerge when different chemical substances interact in a complicated manner, following a structured mathematical framework (Gierer-Meinhardt model), which helps explain how these patterns develop over time. The production of animal stripes on the skin and the organization of embryonic development are biological processes that usually involve these patterns. The present study conducts a detailed mathematical analysis of the Gierer-Meinhardt model by incorporating activation function such as radial basis function. The findings of the present study indicate that the radial basis function neural network is an effective tool for analyzing such complex mathematical models. By correlating the well-established biological models with computational tools like the ANN-RBF networks, new opportunities are created for examining the intricacy of living systems, and the foundation for further research in developmental biology and other fields.