Computation in the brain

The brain - that's my second most favourite organ! - Woody Allen

The Brain as an Information Processing System

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.)

	processing elements	element size	energy use	processing speed	style of computation	fault tolerant	learns	intelligent, conscious
	1014 synapses	10-6 m	30 W	100 Hz	parallel, distributed	yes	yes	usually
	108 transistors	10-6 m	30 W (CPU)	109 Hz	serial, centralized	no	a little	not (yet)

As a discipline of Artificial Intelligence, Neural Networks attempt to bring computers a little closer to the brain's capabilities by imitating certain aspects of information processing in the brain, in a highly simplified way.

Neural Networks in the Brain

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 feedforward 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 neighbours. 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)
• 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 neurotransmittors, 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 neurotransmittor available, the number and arrangement of receptors, amount of neurotransmittor 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.

Long-term Potentiation:

An enduring (>1 hour) increase in synaptic efficacy that results from high-frequency stimulation of an afferent (input) pathway

Hebbs Postulate:

"When an axon of cell A... excites[s] cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells so that A's efficiency as one of the cells firing B is increased."

Bliss and Lomo discovered LTP in the hippocampus in 1973

Points to note about LTP:

• Synapses become more or less important over time (plasticity)
• LTP is based on experience
• LTP is based only on local information (Hebb's postulate)

Summary

The following properties of nervous systems will be of particular interest in our neurally-inspired models:

• parallel, distributed information processing
• high degree of connectivity among basic units
• connections are modifiable based on experience
• learning is a constant process, and usually unsupervised
• learning is based only on local information
• performance degrades gracefully if some units are removed
• etc..........

Further surfing: The Nervous System - a great introduction, many pictures

[Top] [Next: Artificial neuron models] [Back to the first page]