Brain is a massively parallel computing machine
There are many people who do not know that neurons in the brain process the information in parallel.
Below is the explanation why the brain is a massively parallel computing machine.
Human eye contains retina, which contains millions of neural cells (called “cones” and “rods”) and all these cells process visual information in parallel. Rods and cones are connected to other neural cells called “bipolar cells” and all these all these bipolar cells process visual information in parallel. Bipolar cells are connected to other neural cells called “ganglion cells” and all these all these ganglion cells process visual information in parallel. The axons of ganglion cells form optic nerve which contains between 770,000 and 1.7 million nerve fibers and all these million of fibers send visual information in parallel. And so on and so on.
|Physical structure of human
retina. <…> The entire retina contains about 7
million cones and 75 to 150 million rods. <…> In the outer the
rods and cones connect to the vertically running bipolar cells, and the
horizontally oriented horizontal cells connect to ganglion cells.
|Each human optic nerve contains between 770,000 and 1.7 million nerve fibers, which are axons of the retinal ganglion cells of one retina.|
From neurobiological point of view the eye is actually part of the
|The retina is actually part
of the brain
that is isolated to serve as a transducer for the conversion of
patterns of light into neuronal signals. The lens of the eye focuses
light on the photoreceptive cells of the retina, which detect the
photons of light and respond by producing neural impulses. These
signals are processed in a hierarchical
fashion by different parts of the brain, from the retina upstream to
central ganglia in the brain.
Hierarchical processing does not contradict to the concept of
Millions of neural cells in retina (cons and rods) receive and process
visual information simultaneously in parallel, then processed
information is passed to the next layer of neural cells (bipolar cells)
where millions of bipolar cells process visual information
simultaneously in parallel, then processed information is passed to the
next layer of neural cells and so on. All these layers also process
information simultaneously in parallel, the only difference is that
different layers process information with different time delay, however
all layers process information simultaneously in parallel and also
every layer has millions of neural cells which process information
simultaneously in parallel. The brain works as massively parallel
networks are also similar to biological neural networks in performing
functions collectively and in parallel
by the units, rather than there being a clear delineation of subtasks
to which various units are assigned.
We will repeat once again – from neurobiological point of view the
eye is actually part of the brain. The neural cells in retina do
process visual information, and this processing can be pretty complex,
as for example the retina of the frog contains “fly detectors”, which
detect the moving fly/insect. The eye of the frog acts as parallel
computing machine which is able to detect moving flies/insects without
involving any other parts of the brain in the computations.
B. Barlow was one of the first investigators to use the concept of the
feature detector to relate the receptive field of a neuron to a
specific animal behavior. In 1953, H.B. Barlow’s electrophysiological
recordings from excised retina of the frog provided the first evidence
for the presence of an inhibitory surround in the receptive field of a
frog’s retinal ganglion cell. In reference to “on-off” ganglion cells –
which respond to both the transition from light to dark and the
transition from dark to light – and also had very restricted receptive
fields of visual angle (about the size of a fly at the distance that
the frog could strike), Barlow stated, “It is difficult to avoid the
conclusion that the ‘on-off’ units are matched to the stimulus and act
as fly detectors”.
There are many people who think that the brain can not run the
software. These people think that only electronic computers can run the
The misunderstanding here is due to the lack of knowledge in the fields of information theory and computer science. The term “software” has a much broader meaning than many people are used to. Usually when people hear the word “software” they associate that word with digital computers which run digital software. However there is a completely separate class of computing machines which is called “analog computers”. Analog computers process information in completely different way than digital computers and also what is important – the software which runs on analog computers has completely different form from digital computers. Instead of digital digits analog computers process information in another form – they process analog signals. As for example, analog computer can process information in the form of pneumatic or hydraulic streams instead of electric signals.
|An analog computer is a form of
computer that uses the continuously changeable aspects of physical
phenomena such as electrical, mechanical, or hydraulic quantities to
model the problem being solved. In contrast, digital computers
represent varying quantities symbolically, as their numerical values
change. As an analog computer does not use discrete values, but rather
continuous values, processes cannot be reliably repeated with exact
equivalence, as they can with Turing machines. Analog computers do not
suffer from the quantization noise inherent in digital computers, but
are limited instead by analog noise.
Analog computers were widely used in scientific and industrial applications where digital computers of the time lacked sufficient performance. Analog computers can have a very wide range of complexity. Slide rules and nomographs are the simplest, while naval gunfire control computers and large hybrid digital/analog computers were among the most complicated. Systems for process control and protective relays used analog computation to perform control and protective functions.
The advent of digital computing and its success made analog computers largely obsolete in 1950s and 1960s, though they remain in use in some specific applications, like the flight computer in aircraft, and for teaching control systems in universities.
Analog computers often have a complicated framework, but they have, at their core, a set of key components which perform the calculations, which the operator manipulates through the computer's framework.
Key hydraulic components might include pipes, valves and containers.
Key mechanical components might include rotating shafts for carrying data within the computer, miter gear differentials, disc/ball/roller integrators, cams (2-D and 3-D), mechanical resolvers and multipliers, and torque servos.
Key electrical/electronic components might include:
● Precision resistors and capacitors
● operational amplifiers
● fixed-function generators
The core mathematical operations used in an electric analog computer are:
● integration with respect to time
What is important here to understand is – in order to process
information you need to have two things: 1) a hardware (computing
machine) which runs 2) software (algorithms which process that
Human brain does process information, which means that by definition human brain contains software which runs on the neural-hardware (neurons of the brain). Human brain is the computing machine which process information in both forms (analog and digital) simultaneously.
Below is an example of the software that runs on an analog computer.
|The V-2 (German:
Vergeltungswaffe 2, "Retribution Weapon 2"), technical name Aggregat-4
(A4), was the world's first long-range guided ballistic missile.
Some later V-2s used "guide beams", radio signals transmitted from the ground, to keep the missile on course, but the first models used a simple analog computer that adjusted the azimuth for the rocket
Below are some more examples.
| Impressions of Analog
Large scale russian special purpose analog computer ZI-S used to solve questions in the field of hydraulics (cf. V. B. Ushakov, "Soviet Trends in Computers for Control of Manufacturing Processes", in "Instruments and Automation", Nov. 1958, p. 1812).
Analog computers at a NASA simulation facility during the development of the control stick for project Mercury.
Analog computer used at NASA for lunar landing simulations.
Below is a textbook “Handbook of analog computation”, please read
the Chapter 3, which explains the analog programming of analog
|Chapter 3. Elementary analog
|Book title: Handbook of analog
computation: Including application of digital control logic.
Author: Maxwell C Gilliland
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