BRAIN MAPPING AND LIMITATIONS OF BRAIN MAPPING TECHNIQUE

Abstract

The branch of neuroscience known as "brain mapping" focuses on the spatial representation of the human brain. It is a cutting-edge biological computational technique that uses a number of different subfields, including computational biology, engineering principles, and resonance imaging, to reveal the intricate and dynamic structures of the brain. It entails the study of the structural anatomy of the brain as well as the brain's sensory nerves utilising non-invasive, collaborative biomedical techniques as well as cellular biology. Modern computational techniques like as 4-D imaging, cognitive sciences, fuzzy logic, etc. are also used. It displays the physiological and anatomical changes that the brain experiences throughout time or as a result of mental illness. The knowledge of various brain learning impairments has improved as a result of ongoing study in this field.
According to research on the brain mapping technique, when a skill is learned, the brain's structural makeup changes, but these changes are only permanent while the skill is being practised. So, based on the research done by various researchers in the field of the Brain mapping approach, we conducted a survey for this paper. After performing the study, we have come to the conclusion that human brain mapping has aided neurologists in better comprehending the human brain and expanding their knowledge of it, thereby enhancing the learning capacities of Homo sapiens.
Sets of feature-sensitive cells, ordered projections between neuronal layers, and ordered maps of abstract characteristics are three different types of neuronal structure that might be referred to as "brain maps." They reflect the essential characteristics of an organism's experiences and environment, making them the most fascinating. Such feature maps are thought to be learned through a process that involves parallel input to neurons in a particular brain area and adaptation of neurons around the cells that respond most strongly to this input. In this paper, a novel mathematical formulation for such adaptation is presented, along with a connection to physiological processes.