Definition of
   a priori




The process of thinking


The thought process - our thinking including what we call knowledge - is built by the two processes synthesis and analysis. These processes are sufficient to create very complex reasoning, which I intend to show below.

That the brain should function aided by only two processes may appear as too simplified, but when the processes are performed at high speed and includes a lot of information they become incredible powerful.

A similar situation can be seen when a computer actually uses only ones and zeroes. In spite of this apparently dull activity, the computer by processing the ones and zeroes at a high speed can reproduce beautiful music or an action packed movie.


Through synthesis of premises a composite concept is created, and from this we may through analysis reach less composite ideas, i.e. less composite concepts or memories from perception.

The complexity is created by that some of these less composite concepts may be different from the original premises, i.e. that the analysis is not a tautology.

The newly formed concepts may then be synthesized into a new composite concept that is different from the previous, and the process may be repeated at lightning speed.


How fast is the brain?


The reason that we are not conscious about these repeated syntheses and analyses is probably that it is not necessary for our survival. We do not need to know the processes, just the conclusions.

The lower levels when the brain associates are probably quite fast, and the higher levels a little slower. But how fast is the brain, actually?

Here is one comment:

its hundred billion (10^11) neurons
and several hundred trillion synaptic connections can process and exchange prodigious amounts of information over a distributed neural network in the matter of milliseconds.

Marois R & Ivanoff J - Capacity limits of information processing in the brain, Trends in Cognitive Sci 9 (2005) p.296-305.

Ok, but how fast is it? As a person outside of the field I may be allowed to perform some type of calculation:


10 000 movies/second

Our brain is incredible capable in performing reasoning, i.e. syntheses and analyses.

Measurements or calculations of its speed are hard or impossible, but the estimation below - that the brain's capacity corresponds to information processing of 10 000 movies each second - may hopefully contribute to an increased fascination about its operation.

In the estimation below a notation like "x10^9" is used. It implies that 9 zeroes should be written after the previous number. "2 x10^2" hence means 200.

In neocortex (outer cerebral cortex) the number of neurons is about 20 billion (20 x10^9) [Pakkenberg, Karlsen]. The total brain contains about 80-90 billion neurons, and about 80% of these are found in i cerebellum (little brain) [Herculano-Houzel, Zorzetto].

Each neuron has about 7 000 synapses [Drachman]. Each synapse signals 1-200 times/second [Churchland], and in this estimation I assume the tentative value of 5. I also suppose that the signal is binary, i.e. that it is "on" or "off".

Together this yields an activity of the neocortex of 20 billion x 7 000 x 5 = 7 x10^14 signals/second. With binary signals and one Byte = 7 bits, the activity corresponds to 100 million MegaByte per second (1 x10^8 MByte/s).

The information amount in a movie is about ten GigaByte or ten thousand MegaByte (1 x10^4 MByte). The information management of the brain, expressed as movies/second, then becomes the activity (1 x10^8 MByte/s) divided with the movie size (1 x10^4 MByte) that gives 10^4 MByte/s.

The estimation hence gives a brain activity corresponding to 10 000 movies per second.



Example: Faded pot plant


A very simplified example about how reasoning may alternate between synthesis and analysis is initiated when I see a "faded pot plant". The discussion is illustrated by the image below.


Synthesis of the concept "faded pot plant"

I know from the past the concept "pot plant" from reasoning based on "plant blades", "green in the window bay", "geranium smells", "the cactus by the window sting", "flowerpot", "grandmother". Somebody has told me that it is called "pot plant".

Some of these concepts have been created through direct perception, and some were created through synthesis or through analysis from premises that are ultimately based on perception.

"A lot of" premises are actually included in the synthesis, some of which are indicated in the image. Please note the discussion about that a concept, e.g. "flowerpot", in our memory exists as a network, not as an object. The concept "flowerpot" then implies many connections between various networks.

Also the concept "faded" I have created earlier, through reasoning from "hanging leaves", "bent stalk", grey flowers and so forth.

Synthesis of the two concepts "faded" and "pot plant" results in an interpretation of what I am presently looking at.


Analysis follows association paths

During analysis of the recently formed concept "faded pot plant" I find earlier formed associations paths are follow these, in a direction opposite to during synthesis, to other partially forgotten perception and less composite concepts.

One association path reaches to "need water", another to "acquired lice" and maybe some emotion tells me that the cat may be involved (again!) in an unpleasant context.

The cause that I most frequently have experienced in similar situations are strongest activated, and I therefore follow the association path "need water" and create this hypothesis.

This hypothesis is tested by that I feel with my finger in the plant's soil and observes that it is dry.

The two perceptions "faded pot plant" and "dry soil" are then synthesised to the conclusion "the pot plant needs water" (and that the cosy, pleasant and cute little cat was completely without guilt).

The additional premise of the total argument hence increased the credibility of the conclusion.

In this example hence perception, synthesis, analysis and hypothesis testing was used to reach a credible conclusion.

Syntheses and analyses in the example "Faded pot plant"



The fantastic complexity of our thought processes may be illustrated by a thought example:

In case we now see an older tired person we may, through the network of syntheses and analyses above, connect "grandmother" and "water". "Grandmother" may then be associated to "older person" and "water" to "nourishment" to "mental nourishment" to "stimulation". In this manner we may come up with the idea that the older person would become less tired in case it would feel stimulated.


In a corresponding manner, although much more far-reaching, syntheses and analyses may form new ideas from our perceptions and memories.

The process here proposed also gives a simple explanation of false memories [Brewin, Otgaar]:

We may add both relevant and false premises to our network of associations as soon as it becomes activated.


Images of the brain


A detailed illustration of a human brain, with 80-90 billion (thousand millions) neurons, will be exciting to see at some time in the future.

Until then we have to be satisfied by images of smaller brains.


Below is shown an image from the 1 millimetre long fruit-fly (Drosophila) with about 10 000 brain neurons.

The image below shows the connections between brain centres and a large number of connections within them.

Electron microscopy image of the brain of the fruit fly Drosophila [Zheng] (courtesy of Philipp Schegel). (Larger image)



Go small!

C. elegans [Wormatlas] is a small (1 mm) nematode with 302 neurons in the brain. Research about it has resulted in at least two Nobel Prizes [wikipedia].

In spite of its brain's small size, the worm is able to move, to feel "smell" of attracting or repelling agents (chemotaxis), to avoid dehydrating surroundings, seek optimal temperature, and move away when touched [White].


"... will be surprised at the complex environmental contingencies that slugs and bugs are capable of learning."

Sweatt JD, Learning and Memory, comprehensive reference vol.4 (2008) p.4.

The image below shows its brain consisting by a nerve ring and a group of neurons at the abdominal part of the nematode.

The brain of a fully grown C. elegans [Witvliet]. (Larger image)
Brewin CR & Andrews B - Creating Memories for False Autobiographical Events in Childhood: A Systematic Review, Appl. Cognit. Psychol. 31 (2017) 2–23.
Churchland et al., Variance as a Signature of Neural Computations during Decision Making, Neuron 69 (2011) 818-831.
Drachman DA - Do we have a brain to spare?, Neurology 64 (2005) 22004-2005.
Herculano-Houzel S - The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost, Proc Natl Acad Sci USA 109 (2012) 10661-10668. TED Talks 2013.
Packenberg B & Gundersen HJ - Neocortical Neuron Number in Humans: Effect of Sex and Age, J Comparative Neurology 284 (1997) 312-320.
Karlsen AS & Pakkenberg B - Total Numbers of Neurons and Glial Cells in Cortex and Basal Ganglia of Aged Brains with Down Syndrome - A Stereological Study, Cerebral Cortex 21 (2011) 2519-2524.
Otgaar et al. - The Potential for False Memories is Bigger than What Brewin and Andrews Suggest, Appl. Cognit. Psychol. 31 (2017) 24–25.
White JG et al. - The Structure of the Nervous System of the Nematode Caenorhabditis Elegans, Phil. Trans. R. Soc. Lond. B 314 (1986) 1-340.
Witvliet 2021 -Connectomes across development reveal principles of brain maturation, bioRxiv preprint. Also in Nature 596 (2021) 257–261.
Zheng et al. - A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster, Cell 174 (2018) 730–743. Video Abstract.
Zorzetto R - Numbers under review, Pesquisa FAPESP 192 (2012) 27-31.