If you want to understand yourself, you will understand the whole universe – Human Brain And Universe
Table of Contents
∘ Similarities between the human brain and the Universe
∘ Unexpected agreement in structural parameters
“Remember, Arthur, if you want to understand the whole universe, you will understand nothing. But if you want to understand yourself, you will understand the whole universe.”
Could there really be similarities between the human brain and the universe? The human brain weighs about 1.5 kilograms and contains about 1oo billion neurons.
Coincidentally, this is roughly the same number as the number of stars in our galaxy, the number of galaxies in the universe and the number of people who have ever lived.
“The human brain is the most complex object known in the universe. And it is he who knows it.”
Actually, this is not such a strange comparison. You may have seen the image below, which is occasionally shared, showing a human neuron and a simulated galaxy cluster side by side. Indeed, the two look surprisingly similar.
But there is much more to the human brain — and to the Universe — than meets the eye. That’s why the first results of the comparison were truly astonishing. Not only were the brain and the cosmic web identical in complexity, but also in structure.
Similarities between the human brain and the Universe
The human brain functions thanks to its vast network of neurons, which is believed to contain around 69 billion neurons. On the other hand, the observable universe consists of a cosmic network of at least 100 billion galaxies.
In both systems, only 30% of their mass consists of galaxies and neurons. In both systems, galaxies and neurons organize themselves into long filaments or nodes between filaments.
Finally, in both systems, 70% of the distribution of mass or energy is made up of components that play a seemingly passive role. To be precise, the brain is made up of 77 per cent water. And 72 per cent of the universe is dark energy.
Based on the common features of the two systems, the researchers compared a simulation of the network of galaxies with parts of the cerebral cortex and cerebellum. Their aim was to observe how matter fluctuations are distributed at such diverse scales.
The researchers used a technique called power spectrum analysis to study the large-scale distribution of galaxies. The power spectrum of an image measures the strength of structural fluctuations at a given spatial scale.
A striking message emerges from the power spectrum plot in Figure 2. The relative distribution of the fluctuations in the two networks is quite similar, with a difference of a few measurements.
The distribution of fluctuations in the cerebellum on scales of 0.1–1 mm is reminiscent of the distribution of galaxies over hundreds of billions of light-years.
The structure of the cortex at the smallest scales available for microscopic observation (about 10 µm) is quite close to that of galaxies on the scale of several hundreds of thousands of light-years.
Unexpected agreement in structural parameters
Power spectrum comparison does not tell us whether the two systems being compared are equally complex. A practical way to estimate the complexity of a network is to measure the difficulty of predicting its behaviour.
This can be done by calculating the amount of information required to create the smallest possible computer program that can make such an estimate.
A recent study shows that the memory of the human brain is around 2.5 petabytes. Another study suggests that the memory capacity needed to store the complexity of the Universe is about 4.3 petabytes.
This similarity in memory capacity means that all the information stored in a human brain could also be encoded into the distribution of galaxies in our Universe.
The team also looked at other morphological features, such as the number of filaments attached to each node. The cosmic network, based on a sample of 3,800 to 4,700 nodes, had an average of 3.8 to 4.1 connections per node.
The human cortex, with 1,800 to 2,000 nodes, had an average of 4.6 to 5.4 connections per node.
The results of this pilot study are very encouraging. Therefore, the researchers believe that new and efficient analysis techniques in both fields will allow a better understanding of the dynamics underlying the temporal evolution of these two systems.
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