The researchers have made an original approach in which they studied neurons in detail, as if they were physic equations.
Is it possible to explain the working mechanisms of the human brain using only the laws of Physics? Researchers from the University of Granada (UGR) have proven so, in an article published in Scientific Reports, published by Nature.
Researchers Joaquín Torres and Joaquín Marro, from Institute Carlos I for Theoretical and Computational Physics, have made an original approach: they have studied neurons in detail, as if they were physic, differential equations, which are related by a series of interactions called synaptic connections.
Their work has allowed to establish a model based on a series of mathematical neural networks which imitate those natural networks of cerebral connexions that make part of our mind.
The researchers have detected and identified with detail up to seven qualitatively different phases or behaviours of the human mind, to whom they have assigned a different colour.
These changes occur when modifying a parameter called D, which describes the amount of 'noise', that is, the total sum of apparently random signals coming from the exterior or from other parts of the central nervous system. These phases include some familiar mental states such as: total or discontinuous rest; total, partial or changing-with-time neural synchronisations; memories recovery; and very dynamic situations which resemble our states of wakefulness and alertness.
Moreover, disrupting the system with a faint signal clearly shows six well defined peaks that mark a transition between the observed phases.
As professors Torres and Marro explain, "we physicists know how to describe singular situations (called phase changes) with mathematical accuracy. This is the case of solidifying water, which adopts a structure so different to that of the starting point that we are no longer talking about water; or when it turns into vapour, which is able to spread without limits until it occupies all the space available when it is heated, even though volume doesn't changes so much."
Phenomenology associated with phase changes is, in practice, even more fascinating than this patterns may suggest, given that, in the real world, spatial and temporal irregularities prevail instead of the ideal balance described by thermodynamics. "This is the case of evolved brains, as shown in recent researches carried out using magnetic resonance, positron emission tomography, encephalographies, and delicate probes," the authors of this paper explain.
This behaviour makes us think in setting simple psychophysical experiments. "The experiments consist in stimulating the brain with a faint signal -such as, for example, lightly blowing air on the eyes- and monitoring how that signal spreads over our neural network while competing against another noise -such as a sound whose volume can be modified," they explain.
The stimulus is supposed to be processed by neurons, and the latter would react by causing synchronised blinking as a response and defence. However, neurons are also being disrupted by the noise D, so they might not be able to properly synchronise with the blows.
The researchers from the UGR have also proven that the emergent properties from this model are strong, that is, they are not much sensitive to possible changes in the details, specifically the ones related to the topological form of the interaction network.
After confirming the versatility and utility of their model, the researchers now expect to adapt it "in order to understand how these emergent phenomenons related with mental functions could change, considering different interaction networks according to the data available for different animal species. This path may lead us to find out what makes us humans so different in relation to our brains," the researchers conclude.
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