An analog computer is a model of what is to be computed. It is built of mechanical, hydraulic or electrical devices to form a simulation of a problem. A mechanical example is the astrolabe which was built by the ancients to predict the heavens. A famous hydraulic example was MONIAC which modeled the
Analog computers are very fast, next to instantaneous, and can record dynamic processes as they are modeled, usually faster than the real processes that are being investigated. This can be thought of as solving a problem that consists of a large number of constraints. What is the one answer that is available while satisfying all the constraints? If all the constraints are physically modeled and exist simultaneously then the answer is simply there – for any input there is an output. In this speed they are very similar to digital computers that have a lot of parallel processors. In fact if there are enough processors to model all the constraints at the same time, then the digital computer has became essentially the same as an analog one.
A type of computing system that is like an analog one or a massively parallel digital one is a neural net. In a neural net there are a large number of processors and they are each connected to all the others. But each connection has a weight that can be varied from effectively no connection to fully connected. These individual strengths of connection between each pair of processors are like a set of constraints. Each particular pattern of input gives a particular pattern of output. If weights are changed, a different output results. Neural nets and digital programs that mimic neural nets are used on problems like speech and pattern recognition and simulations of brain functions. Not only are neural nets fast and parallel but they can be made so that weighting changes are automated to give a learning computer.
Stepwise algorithms, sequential programs, on conventional computers are nothing like a brain. We have to look at analog, parallel and networked computers to see something that resembles the brain.