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Most atoms are made from positively charged protons, neutral neutrons and negatively charged electrons. Positronium is an exotic atom composed of a single negative electron and a positively charged antimatter positron. It is naturally very short-lived, but researchers including those from the University of Tokyo successfully cooled and slowed down samples of positronium using carefully tuned lasers.

In an exciting development for quantum computing, researchers from the University of Chicago’s Department of Computer Science, Pritzker School of Molecular Engineering, and Argonne National Laboratory have introduced a classical algorithm that simulates Gaussian boson sampling (GBS) experiments.

Yet, the current flow along these topologically protected, one-dimensional edges has proven to be far from robust. With the QAHE breaking down in magnetically doped topological insulators at temperatures higher than 1 Kelvin, well below the temperatures predicted by theory.

A new class of materials, known as intrinsic magnetic topological insulators (MTIs), for example MnBi2Te4, possess both non-trivial topology and intrinsic magnetism and are predicted to offer more robust QAHE at higher temperatures than magnetically doped topological insulators.

In MnBi2Te4 it has been shown that the QAHE can survive up to 1.4 K, and interestingly, this can rise to 6.5 K with the application of stabilizing magnetic fields, providing hints at the mechanisms that are driving the breakdown of topological protection.

Past neuroscience studies have consistently highlighted the profound changes that the human brain undergoes throughout childhood and adolescence. These efforts have uncovered various stages of development, during which the brain’s organization evolves to support increasingly complex cognitive functions, gradually shifting from a focus on somatosensory/motor and visual processing to more advanced mental capabilities.

These stages of brain development and their underlying neurobiological processes have been closely studied and are now relatively well-understood. In contrast, the contributions of specific functional networks (i.e., interconnected that collectively serve specific functions) to the brain’s maturation process remain poorly delineated.

Researchers at Yale University, National University of Singapore and Beijing Normal University carried out a study investigating the extent to which individual functional networks contribute to the maturation of the brain and the gradual acquisition of new cognitive abilities before adulthood.

With maps of the connections between neurons and artificial intelligence methods, researchers can now do what they never thought possible: predict the activity of individual neurons without making a single measurement in a living brain.

For decades, neuroscientists have spent countless hours in the lab painstakingly measuring the activity of neurons in living animals to tease out how the brain enables behavior. These experiments have yielded groundbreaking insights into how the brain works, but they have only scratched the surface, leaving much of the brain unexplored.

Now, researchers are using artificial intelligence and the connectome—a map of neurons and their connections created from —to predict the role of neurons in the living brain. Their paper has been published in the journal Nature.

In this sense, the cemi theory incorporates Chalmers’ (Chalmers 1995) ‘double-aspect’ principle that information has both a physical, and a phenomenal or experiential aspect. At the particulate level, a molecule of the neurotransmitter glutamate encodes bond energies, angles, etc. but nothing extrinsic to itself. Awareness makes no sense for this kind matter-encoded information: what can glutamate be aware of except itself? Conversely, at the wave level, information encoded in physical fields is physically unified and can encode extrinsic information, as utilized in TV and radio signals. This EM field-based information will, according to the double-aspect principle, be a suitable substrate for experience. As proposed in my earlier paper (McFadden 2002a) ‘awareness will be a property of any system in which information is integrated into an information field that is complex enough to encode representations of real objects in the outside world (such as a face)’. Nevertheless, awareness is meaningless unless it can communicate so only fields that have access to a motor system, such as the cemi field, are candidates for any scientific notion of consciousness.

I previously proposed (McFadden 2013b), that complex information acquires its meaning, in the sense of binding of all of the varied aspects of a mental object, in the brain’s EM field. Here, I extend this idea to propose that meaning is an algorithm experienced, in its entirety from problem to its solution, as a single percept in the global workspace of brain’s EM field. This is where distributed information encoded in millions of physically separated neurons comes together. It is where Shakespeare’s words are turned into his poetry. It is also, where problems and solutions, such as how to untangle a rope from the wheels of a bicycle, are grasped in their entirety.

There are of course many unanswered questions, such as degree and extent of synchrony required to encode conscious thoughts, the influence of drugs or anaesthetics on the cemi field or whether cemi fields are causally active in animal brains. Yet the cemi theory provides a new paradigm in which consciousness is rooted in an entirely physical, measurable and artificially malleable physical structure and is amenable to experimental testing. The cemi field theory thereby delivers a kind of dualism, but it is a scientific dualism built on the distinction between matter and energy, rather than matter and spirit. Consciousness is what algorithms that exist simultaneously in the space of the brain’s EM field, feel like.