Computational neuroscience is a specific branch of neuroscience (a broad overall term for the study of the brain) which deals with the information-processing capabilities of neural structures. This field hones in on the chemicals, proteins, and brain architecture responsible for processing information in the brain.

Computational neuroscience is critical to understanding the methods by which the brains of living things work in a real sense. It is an inter-disciplinary study. It explores brain development over time, the processing of sensations, creation and retention of memories, formation of behavioral patterns.

The holy grail of computational neuroscience is the larger question of where human consciousness comes from, and what structures are responsible for it. This question remains unanswered for the time being.

The field includes not just study of humans but of animals of all kinds. Comparing animal brains to human brains is one experimental cutting edge of this field. All the above are studied both biologically via tissue samples, and theoretically via applied mathematics.

Eric L. Schwartz, born in 1947, is credited with coining the term "computational neuroscience." He studied how visual images from the eyes are communicated to the brain in monkeys. Eric reduced the structures he studied into mathematical theory in the seventies, which gained him acclaim in the scientific community. He went on to apply his findings to the human visual system.

One of the seminal discoveries was the Hodgkin-Huxley model. Developed by Alan Lloyd Hodgkin and Andrew Fielding Huxley, the model explains, via mathematical formulae and diagrams, the structure and functionality of neurons. Neurons are cells which transfer messages throughout the brain via electrical impulses. Understanding how neurons carry information can teach scientists a lot about human brain function.

Computational neuroscience has seen many breakthroughs in recent decades, though there is still a lot of unanswered questions in this realm. For instance, scientists understand that humans can recognize a large amount of faces with ease, but they do not yet understand what "machinery" is involved in this process.

The study has wide applications in medicine, and informs modern psychology and psychiatry. Studying the electrical and chemical processes of the brain can help with the diagnosis and treatment of addictions, learning disorders, and psychiatric diseases.

It also has applications outside of the human brain. Modern breakthroughs in machine learning, or creating computers which can "learn" new functions over time without the need for a programmer to input them, are changing the face of business and technology. Scientists can apply cognitive features of the human brain to computers to advance this discipline.

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