Brain

The brain’s intrinsic organization into functional networks has been assessed using imaging techniques, such as functional magnetic resonance imaging (fMRI). In several recent studies, the dynamic functional connectivity (dFC) of these networks was analyzed using a graph theory approach to extract features that characterize their topology over time. The question arises whether the features captured can be explained by the spatial constraints determined by the brain’s underlying structure[1], or if functional coactivation is to some extent responsible for the patterns found.

The hypothesis of “quantum coherent brain dynamics” was introduced by Ricciardi and Umezawa in the late sixties [1]. In the following years, the hypothesis was further advanced to decipher behavior and memory effects [2,3]. According to the proposition, the coherent quantum functionality of the brain is founded on the interaction between a large number of 50 μm spatially distributed coherent quantum states, known as “tunneling photons” and the vector potential of the electromagnetic field.

Alzheimer’s Disease (AD) is the most common type of dementia and a tremendous source of suffering worldwide. Since available treatments exclusively address symptoms but not the disease course, preventive interventions are of vital importance. And due to the multicausality of AD, computational systems models will likely be crucial for informing the design of prevention trials.

The human brain provides a prototype for how diverse collective dynamics and functionality can emerge from a complex network of relatively simple, non-linearly interacting components [1]. Neuromorphic networks formed by self-assembly of polymer-coated inorganic nanowires mimic these ingredients, with the non-linear switching of nanoscale junctions coupled by a disordered, recurrent network topology [2]. Experimental conductance time-series unveiled rich electrical switching dynamics and a phase transition between a low-conducting quiescent and highly-conducting active state.