The Brain-Computer Metaphor

We use a metaphor between brains and computers in a great many situations. This metaphor can be helpful but it can also mislead us. Brains and computers are very different and so any use of the brain-computer metaphor should be justified as useful, not just assumed to be so.

Some people assume that because neurons are either ‘firing’ or not firing, that the brain has a digital mode of operation. This is far from true:

  1. The rate of firing is often the most important aspect of a neuron’s activity and this is a graded activity and not an all or nothing one. A high firing rate gives a strong signal and a low firing rate gives a weak signal. This is not an on-or-off digital signal.
  2. The effect of a firing neuron on another neuron at a synapse can be to excite or inhibit the firing of the second neuron. So even in situations that are not graded, the signal is not binary.
  3. A neuron can have few to many synapses with another neuron and so the effect of one neuron on another is usually graded. This variable number of synapses changes with learning and forming memories.
  4. Other non-neuron cells (some glia cells) do not ‘fire’ but are probably always graded in their effects and take part in signaling in the brain in a not very well understood way. Glia out number the 100 billion neurons by 10 to 1.
  5. The timing of ‘firing’ of neurons can change the nature of the signal. Simultaneous firing is required for some effects. Timing effects may be graded.
  6. Neuron signaling happens in an environment of potent chemicals, electrical fields and magnetic fields. The effect of this environment is graded.

 It takes a lot a circuitry to mimic a neuron with all its in-coming synapses (where it is the post-synaptic cell) and all its out-going synapses (where it is the pre-synaptic cell). A neuron is not a simple electronic switch, or even just a logic gate, it is a complex device.

 Some people assume that because brains can solve problems and the like, they use algorithms or step-by-step procedures. There is a general idea abroad that a Turing machine is a Turing machine and therefore all types of computing can be translated into all others. I believe that there is a big difference between sequential algorithms and some massively parallel operations. The way our present day computers work is sequential (one small operation at a time) or moderately parallel (from a couple to a couple hundred sequential algorithms working simultaneously and exchanging information at intervals), that is to say they use algorithms.

 The brain, on the other hand, incorporates feedback to an almost incomprehensible level. If a nerve track goes from region A to region B there is more than likely another track going from B to A. For example the signals from the retina go to a ‘map’ of the retina in an area called the thalamus. From there nerves take signals to the optic lobe of the neo-cortex where they are mapped again and on to other retinal maps. The neo-cortex neurons send signals back to the thalamus and to the matching part of the other hemisphere. In fact, more or less wherever the neo-cortex sends signals, it also gets signals back from that same place. These loops are basically overlapping parallel feedback loops. Events in such systems cannot be traced sequentially. Signals reverberate until a stable configuration is found (or rattle forever presumably). The brain’s operations are simply not understandable as digital computer programs. The operations are not even that similar to analog computer patch boards. I do not mean that computers cannot be made to mimic some aspects of the brain, but simply that we have to be careful how we interpret such simulations.

 Finally, computers are general machines built to run programs. The nature of the program, a word processor, a drawing program, a spreadsheet, is not important. A brain on the other hand is an organ in a living organism and so its functions are biological. It does not have a general architecture like a computer but a very specific architecture for achieving very specific ends. It is concerned with keeping its organism alive. Computers are occasionally built for very specific purposes but not on anywhere near the scale of specificity seen in brains.

 We must use the convenient metaphor between computers and brains with caution. We would also do well to remember that ‘sufficient quantity has a quality all its own’.

 

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