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Einstein's equations of general relativity (GR) can describe the connection between events within a given hypervolume of size L larger than the Planck length L P in terms of wormhole connections where metric fluctuations give rise to an indetermination relationship that involves the Riemann curvature tensor. At low energies (when L ≫ L P ), these connections behave like an exchange of a virtual graviton with wavelength λ G = L as if gravitation were an emergent physical property. Down to Planck scales, wormholes avoid the gravitational collapse and any superposition of events or space-times become indistinguishable. These properties of Einstein's equations can find connections with the novel picture of quantum gravity (QG) known as the "Einstein-Rosen (ER) = Einstein-Podolski-Rosen (EPR)" (ER = EPR) conjecture proposed by Susskind and Maldacena in Anti-de-Sitter (AdS) space-times in their equivalence with conformal field theories (CFTs). In this scenario, non-traversable wormhole connections of two or more distant events in space-time through Einstein-Rosen (ER) wormholes that are solutions of the equations of GR, are supposed to be equivalent to events connected with non-local Einstein-Podolski-Rosen (EPR) entangled states that instead belong to the language of quantum mechanics. Our findings suggest that if the ER = EPR conjecture is valid, it can be extended to other different types of space-times and that gravity and space-time could be emergent physical quantities if the exchange of a virtual graviton between events can be considered connected by ER wormholes equivalent to entanglement connections.

The study of brain dynamics currently utilizes the new features of nanobiotechnology and bioengineering. New geometric and analytical approaches appear very promising in all scientific areas, particularly in the study of brain processes. Efforts to engage in deep comprehension lead to a change in the inner brain parameters, in order to mimic the external transformation by the proper use of sensors and effectors. This paper highlights some crossing research areas of natural computing, nanotechnology, and brain modeling and considers two interesting theoretical approaches related to brain dynamics: (a) the memory in neural network, not as a passive element for storing information, but integrated in the neural parameters as synaptic conductances; and (b) a new transport model based on analytical expressions of the most important transport parameters, which works from sub-pico-level to macro-level, able both to understand existing data and to give new predictions. Complex biological systems are highly dependent on the context, which suggests a "more nature-oriented" computational philosophy.

The announcement of "Artificial Life" by the Craig Venter group, and the media stir that arose from the news, provoked thoughts about the current technologies in contemporary science and the cultural tension of such projections on the media. The increasingly blurred boundaries between specialist and generalist media, while promising a wider appreciation of scientific discovery, potentially allow unrealistic, ideological claims to dictate scientific research. This is particularly evident in biology, where the pervading paradigm is still dominated by a physically naïve reductionism in which the only relevant causative layer is the molecular one. The reductionist hypothesis is that everything one observes is the result of an underlying molecular mechanism almost independent of the context in which it operates. Molecular mechanisms are often necessarily studied in isolation and therefore operate in unnatural conditions. The mechanistic view of biological regulation implies that we think of genes as intelligent agents. Here we try to critically analyze the motivations behind the spread of such unrealistic simplifications.

A contemporary pathology of science is outlined. This pathology suggests that "previous knowledge" drastically limits innovative thinking in science. In very raw "Bayesian" terms it is affirmed that a too rich and flexible a priori knowledge is detrimental to the appreciation of novelty coming from experimental results by both lowering the relative weight assigned to a posteriori contrasting evidence and adapting potentially revolutionary findings to an already existing frame.