Computation in the brain

The Brain as an Information Processing System

The human brain contains about 10 billion nerve cells, or neurons. On average, each neuron is connected to other neurons through about 10 000 synapses. (The actual figures vary greatly, depending on the local neuroanatomy.) The brain's network of neurons forms a massively parallel information processing system. These contrasts with conventional computers, in which a single processor executes a single series of instructions. Against this, consider the time taken for each elementary operation: neurons typically operate at a maximum rate of about 100 Hz, while a conventional CPU carries out several hundred million machine level operations per second. Despite of being built with very slow hardware, the brain has quite remarkable capabilities:

Its performance tends to degrade gracefully under partial damage. In contrast, most programs and engineered systems are brittle: if you remove some arbitrary parts, very likely the whole will cease to function. It can learn (reorganize itself) from experience. This means that partial recovery from damage is possible if healthy units can learn to take over the functions previously carried out by the damaged areas.

It performs massively parallel computations extremely efficiently. For example, complex visual perception occurs within less than 100 ms, that is, 10 processing steps! It supports our intelligence and self-awareness. (Nobody knows yet how this occurs.)

Neural Networks in the Brain

Some brain areas the brain is not homogeneous. At the largest anatomical scale, we distinguish cortex, midbrain, brainstem, and cerebellum. Each of these can be hierarchically subdivided into many regions, and areas within each region, either according to the anatomical structure of the neural networks within it, or according to the function performed by them. The overall pattern of projections (bundles of neural connections) between areas is extremely complex, and only partially known. The best mapped (and largest) system in the human brain is the visual system, where the first 10 or 11 processing stages have been identified. We distinguish feed forward projections that go from earlier processing stages (near the sensory input) to later ones (near the motor output), from feedback connections that go in the opposite direction. In addition to these long-range connections, neurons also link up with many thousands of their neighbors. In this way they form very dense, complex local networks:

Neurons and Synapses

The basic computational unit in the nervous system is the nerve cell, or neuron. A neuron has:

* Dendrites (inputs) a neuron

* Cell body

* Axon (output)

A neuron receives input from other neurons (typically many thousands). Inputs sum (approximately). Once input exceeds a critical level, the neuron discharges a spike - an electrical pulse that travels from the body, down the axon, to the next neuron(s) (or other receptors). This spiking event is also called depolarization, and is followed by a refractory period, during which the neuron is unable to fire. The axon endings (Output Zone) almost touch the dendrites or cell body of the next neuron. Transmission of an electrical signal from one neuron to the next is effected by neurotransmitters, chemicals which are released from the first neuron and which bind to receptors in the second. This link is called a synapse. The extent to which the signal from one neuron is passed on to the next depends on many factors, e.g. the amount of neurotransmitter available, the number and arrangement of receptors, amount of neurotransmitter reabsorbed, etc.

Synaptic Learning

Brains learn. Of course. From what we know of neuronal structures, one way brains learn is by altering the strengths of connections between neurons, and by adding or deleting connections between neurons. Furthermore, they learn "on-line", based on experience, and typically without the benefit of a benevolent teacher. The efficacy of a synapse can change as a result of experience, providing both memory and learning through long-term potentiation. One way this happens is through release of more neurotransmitter. Many other changes may also be involved.