The universe could be a neural network — an interconnected computational system similar in structure to the human brain — a controversial theory has proposed.
As created by computer scientists, artificial neural networks are made up of various nodes — equivalent to biological neurons — that process and pass on signals.
The network can change as it is used — such as by increasing the weight given to certain nodes and connections — allowing it to 'learn' as it goes along.
For example, given a set of cat pictures to study, a network can learn to pick out characteristic cat features on its own — and so tell them apart from other animals.
However, physicist Vitaly Vanchurin of the University of Minnesota Duluth believes that — on a fundamental level — everything we know may be one of these systems.
The notion has been proposed as a way to reconcile areas of so-called 'classical' physics with those of quantum mechanics — a long-standing problem in physics.
The universe could be a neural network — an interconnected computational system similar in structure to the human brain — a controversial theory has proposed (stock image)
'We are not just saying that the artificial neural networks can be useful for analysing physical systems, or for discovering physical laws — we are saying that this is how the world around us actually works,' Professor Vanchurin wrote in his paper.
'This is a very bold claim,' he conceded.
'It could be considered as a proposal for the theory of everything, and as such it should be easy to prove it wrong.'
'All that is needed is to find a physical phenomenon which cannot be described by neural networks. Unfortunately, [this] is easier said than done.'
When considering the workings of the universe on a large scale, physicists use a particular set of theories as tools.
These are 'classical mechanics' — built on Newton's laws of motion — and Einstein's theories of relativity, which explain the relationship between space and time, and how mass distorts the fabric of spacetime to create gravitational effects.
To explain phenomena on the atomic and subatomic scales, however, physicists have found that the universe is better explained by so-called 'quantum mechanics'.
In this theory, quantities like energy and momentum are restricted to having discrete, not continuous, values (known as 'quanta'), all objects have the properties of both particles and waves — and, finally, that measuring a system changes it.
This last point, the essence of Heisenberg's 'uncertainty principle', means that certain linked properties — such as an object's position and velocity — cannot both be precisely known at the same time, bringing probabilities into play.
While these theories explain the universe very well on their own scales, physicists have long struggled to reconcile them together into a universal theory — a challenge sometimes dubbed 'the problem of quantum gravity'.
For the two theories to mesh, gravity — described by general relativity as the curving of spacetime by matter/energy — would likely need to be made up of quanta and therefore have its own elementary particle, the graviton.
Unfortunately, the effects generated by a single graviton on matter would be extraordinarily weak — making theories of quantum gravity seemingly impossible to test and ultimately determine which, if any, are correct.
Instead of trying to reconcile general relativity and quantum mechanics into one fundamental universal theory, however, the neural network idea suggests that the behaviours seen in both theories emerge from something deeper.
In his study, Professor Vanchurin set out to create a model of how neural networks work — in particular, in a system with a large number of individual nodes.
He says that, in certain conditions — near equilibrium — the learning behaviour of a neural network can be approximately explained with the equations of quantum mechanics, but further away the laws of classical physics instead come