3.1.5 Computational modelling and data-analytics

Computational Modelling and Data-Analytic Descriptions

Computational models control different computer programs in order to stimulate and learn about complex systems by introducing mechanistic and algorithmic approaches. Computational models help in diverse areas spanning biology, chemistry, physics, psychology, cognitive sciences, and economics. The systems that are being studied have nonlinear complex systems and for them, analytical intuitive solutions are not present. Experimentation is done with the model in order to adjust the parameters of a system based on a computer. Some examples of computational models are molecular folding protein models, simulator models, and neural network models.

Computer modelling can allow a researcher or scientist to conduct thousands of different simulated experiences. The laboratory experiments are identified by computer experiments in order to solve the problem that is being under study. However, computational models have the ability to study biological systems at different levels. In neurosciences, it is obvious that data science plays an important role in single-cell recording, EEG, fMRI, and local field potentials. The necessary skills demand the ability to manipulate, understand, and visualize structures of datasets. This is considered a complex skill within research. It is clear that large datasets (built by different labs) are required to provide reliable inferences on the neuroscientific processes. These inferences are also based on computational models because they are responsible for identifying and portraying the data patterns.  Now, machine learning and statistical models are highly used in neuroscience (Taber et al, 1996).

References:

(1) Taber, C.S., Timpone, R.J. and Timpone, R.J. (1996). Computational Modeling. [online] Google Books. SAGE. Available at: https://books.google.co.uk/books?hl=en&lr=&id=-VeO1XuZ3uAC&oi=fnd&pg=PA18&dq=Taber [Accessed 3 Jan. 2023].