martes, 27 de junio de 2017

See Hypervelocity stars arosinhg from the Milky Way

ARTIFICIAL BRAIN HELPS GAIA CATCH SPEEDING STARS

26 June 2017
With the help of software that mimics a human brain, ESA's Gaia satellite spotted six stars zipping at high speed from the centre of our Galaxy to its outskirts. 

This could provide key information about some of the most obscure regions of the Milky Way.
Stars speeding through the Galaxy. Credit: ESA, CC BY-SA 3.0 IGO
Our galactic home, the Milky Way, houses more than a hundred billion stars, all kept together by gravity. Most are located in a flattened structure – the Galactic disc – with a bulge at its centre, while the remaining stars are distributed in a wider spherical halo extending out to about 650 000 light-years from the centre.
Stars are not motionless in the Galaxy but move around its centre with a variety of velocities depending on their location – for example, the Sun orbits at about 220 km/s, while the average in the halo is of about 150 km/s.
Occasionally, a few stars exceed these already quite impressive velocities.
Some are accelerated by a close stellar encounter or the supernova explosion of a stellar companion, resulting in runaway stars with speeds up to a few hundred km/s above the average.
A new class of high-speed stars was discovered just over a decade ago. Swooping through the Galaxy at several hundred of km/s, they are the result of past interactions with the supermassive black hole that sits at the centre of the Milky Way and, with a mass of four million Suns, governs the orbits of stars in its vicinity.
"These hypervelocity stars are extremely important to study the overall structure of our Milky Way," says Elena Maria Rossi from Leiden University in the Netherlands, who presented Gaia's discovery of six new such stars today at the European Week of Astronomy and Space Science in Prague, Czech Republic.
Catching speeding stars. Click here for details and large versions of the video.
Credit: ESA/Gaia/DPAC
"These are stars that have travelled great distances through the Galaxy but can be traced back to its core – an area so dense and obscured by interstellar gas and dust that it is normally very difficult to observe – so they yield crucial information about the gravitational field of the Milky Way from the centre to its outskirts."
Unfortunately, fast-moving stars are extremely difficult to find in the stellar haystack of the Milky Way, as current surveys list the speed of at most a few hundred thousand stars.
Artist's impression of Gaia. Credit: ESA/ATG medialab; background image: ESO/S. Brunier
To find them, scientists have been looking for young, massive stars that would stand out as interlopers in the old stellar population of the Galactic halo. Given away by their out-of-place age, these stars are likely to have received an extra kick to reach the halo. Further measurements of their speeds and estimates of their past paths can confirm if they are indeed hypervelocity stars that were shoved away from the centre of the Milky Way.
So far, only 20 such stars have been spotted. Owing to the specific selection of this method, these are all young stars with a mass 2.5 to 4 times that of the Sun. However, scientists believe that many more stars of other ages or masses are speeding through the Galaxy but remain unrevealed by this type of search.
The billion-star census being performed by Gaia offers a unique opportunity, so Elena and her collaborators started wondering how to use such a vast dataset to optimise the search for fast-moving stars.
After testing various methods, they turned to software through which the computer learns from previous experience.
"In the end, we chose to use an artificial neural network, which is software designed to mimic how our brain works," explains Tommaso Marchetti, PhD student at Leiden University and lead author of the paper describing the results published in Monthly Notices of the Royal Astronomical Society.
"After proper 'training', it can learn how to recognise certain objects or patterns in a huge dataset. In our case, we taught it to spot hypervelocity stars in a stellar catalogue like the one compiled with Gaia."
As part of Elena's research project to study these stars, the team started developing and training this program in the first half of 2016, in order to be ready for the first release of Gaia data a few months later, on 14 September.
Gaia's first sky map. Credit: ESA/Gaia/DPAC. Acknowledgement: A. Moitinho & M. Barros (CENTRA – University of Lisbon), on behalf of DPAC.
Besides a map of over a billion stellar positions, this first release included a smaller catalogue with distances and motions for two million stars, combining observations from Gaia's first year with those from ESA's Hipparcos mission, which charted the sky more than two decades ago. Referred to as the Tycho–Gaia Astrometric Solution, or TGAS, this resource is a taster for future catalogues that will be based solely on Gaia data.
"On the day of the data release, we ran our brand new algorithm on the two million stars of TGAS," says Elena.
"In just one hour, the artificial brain had already reduced the dataset to some 20 000 potential high-speed stars, reducing its size to about 1%.
"A further selection including only measurements above a certain precision in distance and motion brought this down to 80 candidate stars."
The team looked at these 80 stars in further detail. Since only information on the star's motion across the sky are included in the TGAS data, they had to find additional clues to infer their velocity, looking at previous stellar catalogues or performing new observations.
"Combining all these data, we found that six stars can be traced back to the Galactic Centre, all with velocities above 360 km/s," says Tommaso.
Most importantly, the scientists succeeded at probing a different population from the 20 stars that were already known: the newly identified stars all have lower masses, similar to the mass of our Sun.
One of the six stars seems to be speeding so fast, at over 500 km/s, that it is no longer bound by the gravity of the Galaxy and will eventually leave. But the other, slightly slower stars, are perhaps even more fascinating, as scientists are eager to learn what slowed them down – the invisible dark matter that is thought to pervade the Milky Way might also have played a role.
While the new program was optimised to search for stars that were accelerated at the centre of the Galaxy, it also identified five of the more traditional runaway stars, which owe their high speeds to stellar encounters elsewhere in the Milky Way.
"This result showcases the great potential of Gaia opening up new avenues to investigate the structure and dynamics of our Galaxy," says Anthony Brown from Leiden University, a co-author on the study and chair of the Gaia Data Processing and Analysis Consortium.
The scientists are looking forward to using data from the next Gaia release, which is planned for April 2018 and will include distances and motions on the sky for over a billion stars, as well as velocities for a subset.
Dealing with a billion stars, rather than the two million explored so far, is an enormous challenge, so the team is busy upgrading their program to handle such a huge catalogue and to uncover the many speeding stars that will be lurking in the data.
"The sheer number of stars probed by Gaia is an exciting but also challenging opportunity for astronomers, and we are glad to see that they are happily embracing the challenge," says Timo Prusti, Gaia project scientist at ESA.

NOTES FOR EDITORS

"An artificial neural network to discover Hypervelocity stars: Candidates in Gaia DR1/TGAS," by T. Marchetti et al., is published in Monthly Notices of the Royal Astronomical Society.
These results were presented today at the European Week of Astronomy and Space Science in Prague, Czech Republic.

FOR FURTHER INFORMATION, PLEASE CONTACT:

Elena Maria Rossi
Leiden Observatory
The Netherlands
Tel: +31 6 8112 1440
Email: emr@strw.leidenuniv.nl
Tommaso Marchetti
Leiden Observatory
The Netherlands
Tel:  +31 6 4776 9205
Email: marchetti@strw.leidenuniv.nl
Anthony Brown
Leiden Observatory, Leiden University
Leiden, The Netherlands
Email: brown@strw.leidenuniv.nl
Timo Prusti
Gaia Project Scientist
European Space Agency
Email: timo.prusti@esa.int
Markus Bauer
ESA Science and Robotic Exploration Communication Officer
Tel: +31 71 565 6799
Mob: +31 61 594 3 954
Email: markus.bauer@esa.int



Last Update: 26 June 2017

 SOURCE: 
http://sci.esa.int/gaia/59263-artificial-brain-helps-gaia-catch-speeding-stars/

viernes, 23 de junio de 2017

A Theory of Reality as More Than the Sum of Its Parts




 New math shows how, contrary to conventional scientific wisdom, conscious beings and other macroscopic entities might have greater influence over the future than does the sum of their microscopic components.        Olena Shmahalo/Quanta Magazine





June 1, 2017



In his 1890 opus, The Principles of Psychology, William James invoked Romeo and Juliet to illustrate what makes conscious beings so different from the particles that make them up.
“Romeo wants Juliet as the filings want the magnet; and if no obstacles intervene he moves towards her by as straight a line as they,” James wrote. “But Romeo and Juliet, if a wall be built between them, do not remain idiotically pressing their faces against its opposite sides like the magnet and the filings. … Romeo soon finds a circuitous way, by scaling the wall or otherwise, of touching Juliet’s lips directly.”
Erik Hoel, a 29-year-old theoretical neuroscientist and writer, quoted the passage in a recent essay in which he laid out his new mathematical explanation of how consciousness and agency arise. The existence of agents — beings with intentions and goal-oriented behavior — has long seemed profoundly at odds with the reductionist assumption that all behavior arises from mechanistic interactions between particles. Agency doesn’t exist among the atoms, and so reductionism suggests agents don’t exist at all: that Romeo’s desires and psychological states are not the real causes of his actions, but merely approximate the unknowably complicated causes and effects between the atoms in his brain and surroundings.
Hoel’s theory, called “causal emergence,” roundly rejects this reductionist assumption.
“Causal emergence is a way of claiming that your agent description is really real,” said Hoel, a postdoctoral researcher at Columbia University who first proposed the idea with Larissa Albantakis and Giulio Tononi of the University of Wisconsin, Madison. “If you just say something like, ‘Oh, my atoms made me do it’ — well, that might not be true. And it might be provably not true.”
 Erik Hoel, a theoretical neuroscientist at Columbia University.
Julia Buntaine

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Using the mathematical language of information theory, Hoel and his collaborators claim to show that new causes — things that produce effects — can emerge at macroscopic scales. They say coarse-grained macroscopic states of a physical system (such as the psychological state of a brain) can have more causal power over the system’s future than a more detailed, fine-grained description of the system possibly could. Macroscopic states, such as desires or beliefs, “are not just shorthand for the real causes,” explained Simon DeDeo, an information theorist and cognitive scientist at Carnegie Mellon University and the Santa Fe Institute who is not involved in the work, “but it’s actually a description of the real causes, and a more fine-grained description would actually miss those causes.”
“To me, that seems like the right way to talk about it,” DeDeo said, “because we do want to attribute causal properties to higher-order events [and] things like mental states.”
Hoel and collaborators have been developing the mathematics behind their idea since 2013. In a May paper in the journal Entropy, Hoel placed causal emergence on a firmer theoretical footing by showing that macro scales gain causal power in exactly the same way, mathematically, that error-correcting codes increase the amount of information that can be sent over information channels. Just as codes reduce noise (and thus uncertainty) in transmitted data — Claude Shannon’s 1948 insight that formed the bedrock of information theory — Hoel claims that macro states also reduce noise and uncertainty in a system’s causal structure, strengthening causal relationships and making the system’s behavior more deterministic.
“I think it’s very significant,” George Ellis, a South African cosmologist who has also written about top-down causation in nature, said of Hoel’s new paper. Ellis thinks causal emergence could account for many emergent phenomena such as superconductivityand topological phases of matter. Collective systems like bird flocks and superorganisms — and even simple structures like crystals and waves — might also exhibit causal emergence, researchers said.
The work on causal emergence is not yet widely known among physicists, who for centuries have taken a reductionist view of nature and largely avoided further philosophical thinking on the matter. But at the interfaces between physics, biology, information theory and philosophy, where puzzles crop up, the new ideas have generated excitement. Their ultimate usefulness in explaining the world and its mysteries — including consciousness, other kinds of emergence, and the relationships between the micro and macro levels of reality — will come down to whether Hoel has nailed the notoriously tricky notion of causation: Namely, what’s a cause? “If you brought 20 practicing scientists into a room and asked what causation was, they would all disagree,” DeDeo said. “We get mixed up about it.”
A Theory of Cause
In a fatal drunk driving accident, what’s the cause of death? Doctors name a ruptured organ, while a psychologist blames impaired decision-making abilities and a sociologist points to permissive attitudes toward alcohol. Biologists, chemists and physicists, in turn, see ever more elemental causes. “Famously, Aristotle had a half-dozen notions of causes,” DeDeo said. “We as scientists have rejected all of them except things being in literal contact, touching and pushing.”
The true causes, to a physicist, are the fundamental forces acting between particles; all effects ripple out from there. Indeed, these forces, when they can be isolated, appear perfectly deterministic and reliable — physicists can predict with high precision the outcomes of particle collisions at the Large Hadron Collider, for instance. In this view, causes and effects become hard to predict from first principles only when there are too many variables to track.
It’s a bit of a bold thing to do to talk about the mathematics of causation.
Simon DeDeo
Furthermore, philosophers have argued that causal power existing at two scales at once would be twice what the world needs; to avoid double-counting, the “exclusion argument” says all causal power must originate at the micro level. But it’s almost always easier to discuss causes and effects in terms of macroscopic entities. When we look for the cause of a fatal car crash, or Romeo’s decision to start climbing, “it doesn’t seem right to go all the way down to microscopic scales of neurons firing,” DeDeo said. “That’s where Erik [Hoel] is jumping in. It’s a bit of a bold thing to do to talk about the mathematics of causation.”
Friendly and large-limbed, Hoel grew up reading books at Jabberwocky, his family’s bookstore in Newburyport, Massachusetts. He studied creative writing as an undergraduate and planned to become a writer. (He still writes fiction and has started a novel.) But he was also drawn to the question of consciousness — what it is, and why and how we have it — because he saw it as an immature scientific subject that allowed for creativity. For graduate school, he went to Madison, Wisconsin, to work with Tononi — the only person at the time, in Hoel’s view, who had a truly scientific theory of consciousness.
Tononi conceives of consciousness as information: bits that are encoded not in the states of individual neurons, but in the complex networking of neurons, which link together in the brain into larger and larger ensembles. Tononi argues that this special “integrated information” corresponds to the unified, integrated state that we experience as subjective awareness. Integrated information theory has gained prominence in the last few years, even as debates have ensued about whether it is an accurate and sufficient proxy for consciousness. But when Hoel first got to Madison in 2010, only the two of them were working on it there.
Giulio Tononi, a neuroscientist and psychiatrist at the University of Wisconsin, Madison, best known for his research on sleep and consciousness.   John Maniaci/UW Health
Tononi tasked Hoel with exploring the general mathematical relationship between scales and information. The scientists later focused on how the amount of integrated information in a neural network changes as you move up the hierarchy of spatiotemporal scales, looking at links between larger and larger groups of neurons. They hoped to figure out which ensemble size might be associated with maximum integrated information — and thus, possibly, with conscious thoughts and decisions. Hoel taught himself information theory and plunged into the philosophical debates around consciousness, reductionism and causation.
Hoel soon saw that understanding how consciousness emerges at macro scales would require a way of quantifying the causal power of brain states. He realized, he said, that “the best measure of causation is in bits.” He also read the works of the computer scientist and philosopher Judea Pearl, who developed a logical language for studying causal relationships in the 1990s called causal calculus. With Albantakis and Tononi, Hoel formalized a measure of causal power called “effective information,” which indicates how effectively a particular state influences the future state of a system. (Effective information can be used to help calculate integrated information, but it is simpler and more general and, as a measure of causal power, does not rely on Tononi’s other ideas about consciousness.)
The researchers showed that in simple models of neural networks, the amount of effective information increases as you coarse-grain over the neurons in the network — that is, treat groups of them as single units. The possible states of these interlinked units form a causal structure, where transitions between states can be mathematically modeled using so-called Markov chains. At a certain macroscopic scale, effective information peaks: This is the scale at which states of the system have the most causal power, predicting future states in the most reliable, effective manner. Coarse-grain further, and you start to lose important details about the system’s causal structure. Tononi and colleagues hypothesize that the scale of peak causation should correspond, in the brain, to the scale of conscious decisions; based on brain imaging studies, Albantakis guesses that this might happen at the scale of neuronal microcolumns, which consist of around 100 neurons.
                Causation is what you need to give structure to the universe.                                   
...Larissa  Albantakis, a theoretical neuroscientist at the University of Wisconsin,  Madison.        Sophia Loschky
Causal emergence is possible, Hoel explained, because of the randomness and redundancy that plagues the base scale of neurons. As a simple example, he said to imagine a network consisting of two groups of 10 neurons each. Each neuron in group A is linked to several neurons in group B, and when a neuron in group A fires, it usually causes one of the B neurons to fire as well. Exactly which linked neuron fires is unpredictable. If, say, the state of group A is {1,0,0,1,1,1,0,1,1,0}, where 1s and 0s represent neurons that do and don’t fire, respectively, the resulting state of group B can have myriad possible combinations of 1s and 0s. On average, six neurons in group B will fire, but which six is nearly random; the micro state is hopelessly indeterministic. Now, imagine that we coarse-grain over the system, so that this time, we group all the A neurons together and simply count the total number that fire. The state of group A is {6}. This state is highly likely to lead to the state of group B also being {6}. The macro state is more reliable and effective; calculations show it has more effective information.
 A real-world example cements the point. “Our life is very noisy,” Hoel said. “If you just give me your atomic state, it may be totally impossible to guess where your future [atomic] state will be in 12 hours. Try running that forward; there’s going to be so much noise, you’d have no idea. Now give a psychological description, or a physiological one: Where are you going to be in 12 hours?” he said (it was mid-day). “You’re going to be asleep — easy. So these higher-level relationships are the things that seem reliable. That would be a super simple example of causal emergence.”
For any given system, effective information peaks at the scale with the largest and most reliable causal structure. In addition to conscious agents, Hoel says this might pick out the natural scales of rocks, tsunamis, planets and all other objects that we normally notice in the world. “And the reason why we’re tuned into them evolutionarily [might be] because they are reliable and effective, but that also means they are causally emergent,” Hoel said.
Brain-imaging experiments are being planned in Madison and New York, where Hoel has joined the lab of the Columbia neuroscientist Rafael Yuste. Both groups will examine the brains of model organisms to try to home in on the spatiotemporal scales that have the most causal control over the future. Brain activity at these scales should most reliably predict future activity. As Hoel put it, “Where does the causal structure of the brain pop out?” If the data support their hypothesis, they’ll see the results as evidence of a more general fact of nature. “Agency or consciousness is where this idea becomes most obvious,” said William Marshall, a postdoctoral researcher in the Wisconsin group. “But if we do find that causal emergence is happening, the reductionist assumption would have to be re-evaluated, and that would have to be applied broadly.”
New Philosophical Thinking
Sara Walker, a physicist and astrobiologist at Arizona State University who studies the origins of life, hopes measures like effective information and integrated information will help define what she sees as the gray scale leading between nonlife and life (with viruses and cell cycles somewhere in the gray area). Walker has been collaborating with Tononi’s team on studies of real and artificial cell cycles, with preliminary indications that integrated information might correlate with being alive.
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In other recent work, the Madison group has developed a way of measuring causal emergence called “black-boxing” that they say works well for something like a single neuron. A neuron isn’t simply the average of its component atoms and so isn’t amenable to coarse-graining. Black-boxing is like putting a box around a neuron and measuring the box’s overall inputs and outputs, instead of assuming anything about its inner workings. “Black-boxing is the truly general form of causal emergence and is especially important for biological and engineering systems,” Tononi said in an email. 
Walker is also a fan of Hoel’s new work tracing effective information and causal emergence to the foundations of information theory and Shannon’s noisy-channel theorem. “We’re in such deep conceptual territory it’s not really clear which direction to go,” she said, “so I think any bifurcations in this general area are good and constructive.”
Robert Bishop, a philosopher and physicist at Wheaton College, said, “My take on EI” —effective information — “is that it can be a useful measure of emergence but likely isn’t the only one.” Hoel’s measure has the charm of being simple, reflecting only reliability and the number of causal relationships, but according to Bishop, it could be one of several proxies for causation that apply in different situations.
Hoel’s ideas do not impress Scott Aaronson, a theoretical computer scientist at the University of Texas, Austin. He says causal emergence isn’t radical in its basic premise. After reading Hoel’s recent essay for the Foundational Questions Institute, “Agent Above, Atom Below” (the one that featured Romeo and Juliet), Aaronson said, “It was hard for me to find anything in the essay that the world’s most orthodox reductionist would disagree with. Yes, of course you want to pass to higher abstraction layers in order to make predictions, and to tell causal stories that are predictively useful — and the essay explains some of the reasons why.”
It didn’t seem so obvious to others, given how the exclusion argument has stymied efforts to get a handle on higher-level causation. Hoel says his arguments go further than Aaronson acknowledges in showing that “higher scales have provably more information and causal influence than their underlying ones. It’s the ‘provably’ part that’s hard and is directly opposite to most reductionist thinking.”
Moreover, causal emergence isn’t merely a claim about our descriptions or “causal stories” about the world, as Aaronson suggests. Hoel and his collaborators aim to show that higher-level causes — as well as agents and other macroscopic things — ontologically exist. The distinction relates to one that the philosopher David Chalmers makes about consciousness: There’s the “easy problem” of how neural circuitry gives rise to complex behaviors, and the “hard problem,” which asks, essentially, what distinguishes conscious beings from lifeless automatons. “Is EI measuring causal power of the kind that we feel that we have in action, the kind that we want our conscious experiences or selves to have?” said Hedda Hassel Mørch, a philosopher at New York University and a protégé of Chalmers’. She says it’s possible that effective information could “track real ontological emergence, but this requires some new philosophical thinking about the nature of laws, powers and how they relate.”
The criticism that hits Hoel and Albantakis the hardest is one physicists sometimes make upon hearing the idea: They assert that noise, the driving force behind causal emergence, doesn’t really exist; noise is just what physicists call all the stuff that their models leave out. “It’s a typical physics point of view,” Albantakis said, that if you knew the exact microscopic state of the entire universe, “then I can predict what happens until the end of time, and there is no reason to talk about something like cause-effect power.”
One rejoinder is that perfect knowledge of the universe isn’t possible, even in principle. But even if the universe could be thought of as a single unit evolving autonomously, this picture wouldn’t be informative. “What is left out there is to identify entities — things that exist,” Albantakis said. Causation “is really the measure or quantity that is necessary to identify where in this whole state of the universe do I have groups of elements that make up entities? … Causation is what you need to give structure to the universe.” Treating causes as real is a necessary tool for making sense of the world.
Maybe we sort of knew all along, as Aaronson contends, that higher scales wrest the controls from lower scales. But if these scientists are right, then causal emergence might be how that works, mathematically. “It’s like we cracked the door open,” Hoel said. “And actually proving that that door is a little bit open is very important. Because anyone can hand-wave and say, yeah, probably, maybe, and so on. But now you can say, ‘Here’s a system [that has these higher-level causal events]; prove me wrong on it.’”
Correction: This article was revised on June 1, 2017, to clarify that Erik Hoel was not Giulio Tononi’s first collaborator on integrated information theory and that Tononi, Hoel and Larissa Albantakis formalized, but did not devise, the measure used to assess causal emergence.

This article was reprinted on Wired.com.




SOURCE: https://www.quantamagazine.org/a-theory-of-reality-as-more-than-the-sum-of-its-parts-20170601/

jueves, 8 de junio de 2017



Descubren en Marruecos los fósiles de 'Homo sapiens' más antiguos, con 315.000 años.....



















RTVE.es / EUROPA PRESS


Los restos más antiguos conocidos de Homo Sapiens han sido hallados en un lugar de Marruecos llamado Yebel Irhoud, unos 150 kilómetros al oeste de Marrakech, según un estudio publicado en la revista especializada Nature. Este descubrimiento cambiaría el origen de nuestra especie, situado hasta ahora en África Oriental, en diferentes puntos de Etiopía, aunque según el trabajo científico los restos confirman que el Homo Sapiens estuvo presente en todo el continente africano.

Los hallazgos datan de hace unos 315.000 años y representan la evidencia fósil más antigua de la especie humana, según los autores.

Tanto los datos genéticos de los seres humanos actuales como los restos fósiles apuntan a un origen africano del Homo sapiens

Anteriormente, los fósiles de esta especie más antiguos y con datación segura eran del sitio de Omo Kibish, en Etiopía, fechado hace 195.000 años. En Herto, también en Etiopía, un fósil de Homo sapiens está fechado hace 160.000 años.
"Solíamos pensar que había una cuna de la humanidad hace 200.000 años en el este de África, pero nuestros nuevos datos revelan que el Homo sapiens se extendió por todo el continente africano hace unos 300.000 años. Mucho antes de la dispersión fuera de África del Homo sapiens, hubo dispersión dentro de África", ha explicado el paleoantropólogo Jean-Jacques Hublin.

Jebel Irhoud es el sitio más antiguo y más rico de homínidos en África

El sitio marroquí de Jebel Irhoud ha sido bien conocido desde la década de 1960 por sus fósiles humanos y por sus artefactos de la Edad de Piedra. Sin embargo, la interpretación de los homínidos de Irhoud ha sido complicada por las persistentes incertidumbres que rodean su edad geológica. El nuevo proyecto de excavación, que comenzó en 2004, dio lugar al descubrimiento in situ de nuevos fósiles de Homo sapiens, aumentando su número de seis a 22.

Estos hallazgos confirman la importancia de Jebel Irhoud como el sitio más antiguo y más rico de homínidos de la Edad Media de la Edad de Piedra en África, una etapa temprana de nuestra especie. Los restos fósiles de Jebel Irhoud comprenden cráneos, dientes y huesos largos de al menos cinco individuos.

Para proporcionar una cronología precisa de estos hallazgos, los investigadores utilizaron el método de datación por termoluminiscencia sobre pedernales calentados encontrados en los mismos depósitos. Estos pedernales eran de hace aproximadamente 300.000 años y, por lo tanto, empujan hacia atrás los orígenes de nuestra especie en 100.000 años.

El cráneo de los seres humanos modernos que viven hoy se caracteriza por una combinación de rasgos que nos distinguen de nuestros parientes fósiles y antepasados: una cara pequeña y una caja del cráneo globular.

Los fósiles de Jebel Irhoud exhiben un rostro y dientes de apariencia moderna y una coraza grande, pero de apariencia más arcaica. Hublin y su equipo utilizaron exploraciones micro tomográficas computarizadas de última generación y análisis de forma estadística basados en cientos de medidas 3D para mostrar que la forma facial de los fósiles de Jebel Irhoud es casi indistinguible de la de los seres humanos modernos que viven hoy.

Sin embargo, en contraste con su morfología facial moderna, la cría de Jebel Irhoud conserva una forma arcaica algo alargada de la cintura. "La forma interna de la caja del cráneo refleja la forma del cerebro", ha señalado el paleontólogo Philipp Gunz, del Instituto Max Planck de Antropología Evolutiva de Leipzig. "Nuestros hallazgos sugieren que la moderna morfología facial humana se estableció desde el principio en la historia de nuestra especie y que la forma cerebral y posiblemente el cerebro, que se desarrolló dentro del linaje del Homo sapiens".


Las comparaciones de ADN antiguo extraído de Neanderthales y denisovanos al ADN de los seres humanos actuales han revelado diferencias en los genes que afectan al cerebro y el sistema nervioso. La morfología y la edad de los fósiles de Jebel Irhoud también corroboran la interpretación de un enigmático cráneo parcial de Florisbad, al sur de África, como uno de los primeros representantes del Homo sapiens.


Conexiones estrechas del Magreb con el resto del continente

Los primeros fósiles Homo sapiens se encuentran en todo el continente africano: Jebel Irhoud, Marruecos (315.000 años), Florisbad, Sudáfrica (260.000 años) y Omo Kibish, Etiopía (195.000 años), lo que indica una compleja historia evolutiva de nuestra especie, posiblemente involucrando a todo el continente africano.

·     Este descubrimiento amplía los orígenes de nuestra especie en 100.000 años
·     Cambiaría la cuna de la humanidad, situada hasta ahora en Etiopía
·     Los restos son cráneos, dientes y huesos largos de al menos cinco individuos




"Se ha descuidado el norte de África en los debates sobre el origen de nuestra especie. Los espectaculares descubrimientos de Jebel Irhoud demuestran las conexiones estrechas del Magreb con el resto del continente africano en el momento de la emergencia del Homo sapiens", dice Abdelouahed Ben-Ncer. Los fósiles se encontraron en depósitos que contenían huesos de animales que mostraban evidencia de haber sido cazados, siendo las gacelas la especie más frecuente.

Las herramientas de piedra asociadas con estos fósiles pertenecen a la Edad Media de Piedra y los artefactos de Jebel Irhoud muestran el uso de técnicas de Levallois y las formas puntiagudas son las más comunes. La mayoría de las herramientas de piedra se hicieron de sílex de alta calidad importados en el sitio. Las hachas de mano, una herramienta comúnmente encontrada en sitios antiguos, no están presentes en Jebel Irhoud.

"Los artefactos de piedra de Jebel Irhoud parecen muy similares a los de depósitos de edad similar en el este y sur de África. Es probable que las innovaciones tecnológicas de la Edad Media de Piedra en África estén relacionadas con la aparición del Homo sapiens", ha dicho el arqueólogo del Instituto Max Planck, Shannon McPherron.

FUENTE: http://www.rtve.es/noticias/20170607/descubren-marruecos-fosiles-homo-sapiens-mas-antiguos-hace-300000-anos/1561647.shtml