Neuroinformatics refers to a research field that focuses on organizing neuroscience data through analytical tools and computational models. It combines data across all scales and levels of neuroscience in order to understand the complex functions of the brain and work toward treatments for brain-related illness. Neuroinformatics involves the techniques and tools for acquiring, sharing, storing, publishing, analyzing, modeling, visualizing and simulating data.
Neuroinformatics helps researchers to work together and share data across different facilities and different countries through the exchange of approaches and tools for integrating and analysing data. This field makes it possible to integrate any type of data across various biological organization levels.
The advancement of neuroinformatics technology facilitates the research done in this field and helps in the free exchange of data and ideas among neurological researchers worldwide.
Neuroinformatics has the following key functions:
The creation of tools and technologies that help neuroscience researchers to effortlessly manage, communicate and share the overall data load in real time. This helps the researchers use the time effectively and make sure that they are working on the most up-to-date data.
The creation of up-to-date tools and software for analyzing the neuroscience data in the best possible way and developing complicated models based on that data.
The development of complicated models of the central nervous system, which helps researchers to understand the functioning of computational processes and perform direct experiments on a model to understand its reaction to different situations and stimulations.
The benefits of neuroinformatics include:
Advancement in neuroscience and improvement in the treatment of several neurological disorders
The enhancement of researchers' knowledge. Neuroinformatics enables them to understand the working pattern of some particular neurological functions by permitting the researchers to trace some specific functions inside the computerized models.
The accomplishment of huge volumes of new data for creating more sophisticated models for testing.