Photo by Ben White on Unsplash

The term inference in Karl Friston’s theory means the derivation of an unknown cause from a known consequence. According to Friston, living beings guess about the cause hidden from them (by the Markov blanket), based on the outcome, which surprises them — precisely by the absence of an obvious cause.

“This surprise can be thought of as a prediction error, which can be used to update the best guess to provide a better prediction”, Friston clarified, helping me to finally understand that a strange event can be also classified as a prediction error although no specific prediction in respect of…

Photo by Ketut Subiyanto from Pexels

A mathematical model of just one neuron can predict earthquake aftershocks using only two parameters and logistic regression (the same one used in the Lotka-Volterra system of equations) with the same or higher accuracy as a deep neural network of six hidden layers of 50 neurons in each, processing over 13 thousand parameters.

The story about this made a lot of noise in narrow circles in 2018–19. Since then, I have been constantly asking myself the question: do modern versions of artificial neural networks have redundant functionality, which makes the basic principle of their work incomprehensible?

It seems to me…

Photo by Andre Moura from Pexels

The simplest mathematical model of a neural network can generate the dynamics of both quantum and classical processes. Thus, the hypothesis that the universe is a neural network can become a unifying theory of everything. Some people call it God’s Algorithm. This is the essence of Vitaly Vanchurin’s hypothesis.

If it were otherwise, humanity would still not be aware of the fact that there are quantum and classical processes. If the neural networks of individual people, at least, could not reproduce the dynamics of these processes, no one would understand that they exist.

It is impossible to remove science from…

Sea angel. Photo © Alexander Semenov, Belomorsk Biological Station named after N. A. Pertsov

Sea angels are snails that have learned to fly. The shell falls off in infancy, and the angel flaps its former crawling leg like a butterfly flaps its wings to soar in the water column or rush for prey.

Pablo Varona, a colleague of Mikhail Rabinovich in the development of concepts of stable heteroclinic channels and winnerless competition, chose, together with his colleagues, an outlandish mollusk as a model to study the transition from sensation to movement in the nervous system due to the simplicity of its structure and behavior.

Although it is hard to call a simple device gravimetric…

Shrinking Brain, Hippocampus, Cognitive Maps and Objective Reality

Photo by Gerd Altmann

Welcome to the Jungle!

It is impossible to find a reliable source of accurate information in the information jungle. The number of sources grows exponentially. Their reliability decreases at approximately the same speed. The only solution remains to independently verify the accuracy of all information received from any source irrespectively of how reliable that source was in the past.

Imagine that the labels with expiry dates on all the products in the supermarket are mixed up. …

Photo by Francesco Ungaro from Pexels

In this chapter, we learn that the brains of our ancestors were growing very rapidly for three million years to become human. The expansion of the surface area of the cerebral cortex launched the positive feedback loop between itself and the cultural evolution of the human species.

About three million years ago the brains of some hominid species began to gain volume much faster than the brains of all mammals including other primates. Since then those hominids had become human species and had tripled the size of their brains that became over six times larger than the brains of any other mammals of similar body size.

Even more importantly, the volume of their brains increased mostly due to the growth of their cortex, a many times folded thin and smooth layer of grey matter consisting of neurons, the brain cells with which we think.

Furthermore, the folding…

Photo by Alex Knight from Pexels

Section One. The Natural Method of Learning

The natural method of learning was discovered at the end of the nineteenth century. Since then it has been rediscovered many times yet it never attracted the interest of mainstream science. It performs computations with “topological constructive objects” instead of numbers. Therefore, it’s hard to embrace it as technology. Yet there seems to be no shortcut to building artificial general intelligence but for understanding the method used by nature. Let’s begin by scrutinizing the natural method’s discovery and rediscoveries in order to grasp its essence.


  1. V. A. Uspenski, A. L. Semyonov (1993) Kolmogorov’s Algorithms or Machines

Chapter One. Cat Scientists

In this chapter, we discuss how Pavlov’s dogs and Thorndike’s cats defined the mainstream of psychology, neuroscience, and artificial intelligence for the Twentieth century and defied the discovery of the method of understanding.

An animal and a…

Photo by Gaurav Bagdi from Pexels

In this chapter, we will see how savage tribes before their close contact with modern humans as well as our prehistoric ancestors were using empiricism and logic to build and keep up to date their picture of the world — the gigantic foundation of modern science.

Claude Levi-Strauss, a French social anthropologist often known as “the father of modern anthropology”, in his book The Savage Mind describes in great detail the ways how savage tribes meticulously classify the world around them. With his deep insight derived from observations of the savage life and the structural analysis of ancient myths, he draws a picture of the overwhelming scientific project undertaken by our distant ancestors in learning from nature around them.

Like cat scientists in the experiments of Edward Thorndike, savage scientists in the descriptions cited by Levi-Strauss were relentlessly classifying external objects through relationships between them.


Photo by Spencer Selover from Pexels

“What, then, does the child think as he makes these discoveries? First of all, he wonders. This feeling of wonderment is the source and inexhaustible fountain-head of his desire for knowledge. It drives the child irresistibly on to solve the mystery, and if in his attempt he encounters a causal relationship, he will not tire of repeating the same experiment ten times, a hundred times, in order to taste the thrill of discovery over and over again. Thus, by a process of incessant labor from day to day, the child eventually develops his world picture, to the degree needed by…

Photo by Bradley Hook from Pexels

In this Chapter, physicist Max Plank and psychologist Jean Piaget explain that the only objective reality accessible to us is a conceptual space created by the power of our intelligence.

Max Planck proposed to use investigating “the most primitive world picture, the naive world picture of the child” as “the best start toward a correct understanding of the scientific world picture.” His description of the way the child creates his world picture is so accurate that I won’t even try to rephrase him. I’ll just point your attention to the similarity between Plank’s description and Pavlov’s notion about the scientific (or animal-like as per Thorndike) method of learning. The child learns by classifying and connecting external stimuli with each other. The scientist learns in the same way. …

Yuri Barzov

Curious about life and intelligence

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store