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Rage Against the Dying of Complexity!



 The aging brain shows a progressive loss of neuropil, which is accompanied by subtle changes in neuronal plasticity, sensory learning and memory. Neurophysiologically, aging attenuates evoked responses—including the mismatch negativity (MMN). This is accompanied by a shift in cortical responsivity from sensory (posterior) regions to executive (anterior) regions, which has been interpreted as a compensatory response for cognitive decline. Theoretical neurobiology offers a simpler explanation for all of these effects—from a Bayesian perspective, as the brain is progressively optimized to model its world, its complexity will decrease. A corollary of this complexity reduction is an attenuation of Bayesian updating or sensory learning. Here we confirmed this hypothesis using magnetoencephalographic recordings of the mismatch negativity elicited in a large cohort of human subjects, in their third to ninth decade. Employing dynamic causal modeling to assay the synaptic mechanisms underlying these non-invasive recordings, we found a selective age-related attenuation of synaptic connectivity changes that underpin rapid sensory learning. In contrast, baseline synaptic connectivity strengths were consistently strong over the decades. Our findings suggest that the lifetime accrual of sensory experience optimizes functional brain architectures to enable efficient and generalizable predictions of the world.



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"The Hidden Power Laws of Ecosystems"



As nature scales, complexity gives way to universal law.




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"A Twisted Path to Equation-Free Prediction"


Complex natural systems defy standard mathematical analysis, so one ecologist is throwing out the equations.




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"Meet the Father of Digital Life"



 In 1953, at the dawn of modern computing, Nils Aall Barricelli played God. Clutching a deck of playing cards in one hand and a stack of punched cards in the other, Barricelli hovered over one of the world’s earliest and most influential computers, the IAS machine, at the Institute for Advanced Study in Princeton, New Jersey. During the day the computer was used to make weather forecasting calculations; at night it was commandeered by the Los Alamos group to calculate ballistics for nuclear weaponry. Barricelli, a maverick mathematician, part Italian and part Norwegian, had finagled time on the computer to model the origins and evolution of life.


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"Evolution May Be Drunk, But It’s Serious About Making Brains"



 "Our brains, perched atop a network of nerve cells that ascend the length of our bodies, are thought to have arisen once in an animal hundreds of millions of years ago and then evolved over time. However, new findings suggest instead that brains and nervous systems originated multiple times from scratch."


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"The Astounding Link Between the P≠NP Problem and the Quantum Nature of Universe"



 The paradox comes about because the radioactive decay is a quantum process and so in a superposition of states until observed. The radioactive atom is both decayed and undecayed at the same time.

But that means the cat must also be in a superposition of alive and dead states until the box is open and the system is observed. In other words, the cat must be both dead and alive at the same time.

Nobody knows why we don’t observe these kinds of strange superpositions in the macroscopic world. For some reason, quantum mechanics just doesn’t work on that scale. And therein lies the mystery, one of the greatest in science.

(...)Bolotin then goes on to show that the problem of solving the Schrödinger equation is at least as hard or harder than any problem in the NP class. This makes it equivalent to many other head-scratchers such as the travelling salesman problem. Computational complexity theorists call these problems NP-hard.


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"A Fundamental Theory to Model the Mind"



 "In 1999, the Danish physicist Per Bak proclaimed to a group of neuroscientists that it had taken him only 10 minutes to determine where the field had gone wrong. Perhaps the brain was less complicated than they thought, he said. Perhaps, he said, the brain worked on the same fundamental principles as a simple sand pile, in which avalanches of various sizes help keep the entire system stable overall — a process he dubbed “self-organized criticality.”


Classical phase transitions require what is known as precise tuning: in the case of water evaporating into vapor, the critical point can only be reached if the temperature and pressure are just right. But Bak proposed a means by which simple, local interactions between the elements of a system could spontaneously reach that critical point — hence the term self-organized criticality.

Think of sand running from the top of an hourglass to the bottom. Grain by grain, the sand accumulates. Eventually, the growing pile reaches a point where it is so unstable that the next grain to fall may cause it to collapse in an avalanche. When a collapse occurs, the base widens, and the sand starts to pile up again — until the mound once again hits the critical point and founders. It is through this series of avalanches of various sizes that the sand pile — a complex system of millions of tiny elements — maintains overall stability.

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Rage against the dying of complexity!




 Theoretical neurobiology offers a simpler explanation for all of these effects—from a Bayesian perspective, as the brain is progressively optimized to model its world, its complexity will decrease. A corollary of this complexity reduction is an attenuation of Bayesian updating or sensory learning. 

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Santa Fe Institute - "Complexity: Life, Scale, & Civilization"


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"Die, selfish gene, die"


 These days, Dawkins makes the news so often for buffoonery that some might wonder how he ever became so celebrated. The Selfish Gene is how. To read this book is to be amazed, entertained, transported. For instance, when Dawkins describes how life might have begun — how a randomly generated strand of chemicals pulled from the ether could happen to become a ‘replicator’, a little machine that starts to build other strands like itself, and then generates organisms to carry it — he creates one of the most thrilling stretches of explanatory writing ever penned. It’s breathtaking.

Dawkins assembles genetics’ dry materials and abstract maths into a rich but orderly landscape through which he guides you with grace, charm, urbanity, and humour. He replicates in prose the process he describes. He gives agency to chemical chains, logic to confounding behaviour. He takes an impossibly complex idea and makes it almost impossible to misunderstand. He reveals the gene as not just the centre of the cell but the centre of all life, agency, and behaviour. By the time you’ve finished his book, or well before that, Dawkins has made of the tiny gene — this replicator, this strip of chemicals little more than an abstraction — a huge, relentlessly turning gearwheel of steel, its teeth driving smaller cogs to make all of life happen. It’s a gorgeous argument. Along with its beauty and other advantageous traits, it is amenable to maths and, at its core, wonderfully simple.

Unfortunately, say Wray, West-Eberhard and others, it’s wrong.

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