The brain as a prototype for computers of tomorrow
The mammalian brain can be described as the most advanced computer in nature. Brains can be thought of as consisting of distributed mini-processors (micro-circuits), with strong but relatively slow communication links and distributed memory. Thus the brain is a prototype for parallel-distributed computation.
The history of computing is often traced back to Alan Turing's 1936 paper ‘On computable numbers, with an application to the Entscheidungsproblem'. He suggested how a machine, using the logic of a person equipped with instructions, paper and a pencil, could read symbols encoded on a string of tape to perform symbolic operations defined by an instructor.
During the Second World War, John von Neumann built an electronic version of Turing's machine. Computers have developed at an astronomic rate since then, but the way in which they work has remained essentially unchanged.
Computer science is now in the transition from essentially serial algorithms, using one or a few computer cores at the time, to genuinely parallel processes operated by massively distributed elements on a continuous basis. This is not too different from the way the brain processes information.
Brain processors operate at speeds that are five to six orders of magnitude slower than modern CPUs, yet they outperform modern computers in a number of ways. The different algorithms used by brains and computers reflect fundamental differences in the hardware they use, and in how they use it. Nevertheless, this does not preclude the possibility of transferring principles from one system to the other. This is what the GRIDMAP project aims to do: We are studying the brain so we can develop better computers. In the process, we will also learn a lot more about the brain.