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Wednesday, February 27, 2019



Taken from PNAS

Among several 3 news, related to the positioning of holistic concepts, extracted from observations made in humans and animals -and their respective validation- to prevent or eliminate cancer. I) The observation that: a) elephants and naked mole rats rarely acquire cancer, while ferrets and dogs acquire it at very high frequencies. In 1977, Richard Peto postulated that cells of   large bodies suffering more cell divisions had more risk of acquiring cancer and, that other small and short-lived: not. But, when Peto studied the incidence of cancer in large animals, he did not find what his theory preached (Peto’s paradox). In 2015, Joshua Schiffman (High Risk Pediatric Cancer Clinic at Huntsman Cancer, University of Utah) and collaborators, discovered that elephants had 40 copies of TP53 suppressor tumor gene (also present in humans and animals), which, when detecting irreparable damage in DNA, they promote the death of the cells involved, avoiding cancer. Now, Schiffman plans to introduce TP53 genes into humans, via nanoparticles. On the other hand, in 2018, Vincent Lynch showed that the elephants also had 11 extra copies of the gene: leukemia inhibitory factor (LIF). A copy of LIF6 is activated by a TP53 in response to DNA damage. b) Naked mole rats (Heterocephalus glaber), a species of mice that live more than 30 years, typically exhibit an extracellular matrix. Keeping some space between the cells they reduce the risk of cancer. c) The resistance to cancer of the South American capybara (Hydrochoerus hydrochaeris), is explained because although the insulin signals of these animals allow them to grow more than their ancestors, the collateral effect is that   regulating (counteracting), this growth, turn their hypervigilant immune system against cancer. In this case, the same growth signals of the capybaras are sequestered by the cancer cells promoting their own growth and proliferation. d) Amy Boddy (University of California, Santa Barbara), argues that during mammal’s pregnancy, the placenta acts as a fetal tissue, which invades the mother's womb, promoting the proliferation of blood vessels and suppressing the maternal immune system so that the mother tolerates cells from genetically different fetuses. In the same way a metastatic tumor suppresses the immune system, so genetically different cells are tolerated. Although sometimes, when a gene regulates more than one function, these can come into conflict. 

II) The theory promoted by Panos Anastasiadis (Department of Cancer Biology on Mayo Clinic's Florida), proposing a close and vital interrelation between the growth of normal cells and their corresponding brake, not existing brakes for cancer cells. For Anastasiadis, the mechanisms that maintain the growth-brake cell system in a normal or altered state reside in the intercellular junctions. He said that until recently, it was thought that these adhesion molecules functioned only as a glue that holds cells together, when in fact their real function is to generate specific structures that produce intercellular communication signals through microRNAs (expression regulators genes). When communication through these intercellular molecular structures is interrupted, tumorigenesis would occur, that’s to say  bad-regulated microRNAs would promote out-of-control cell growths. In this regard Anastasiadis said that when normal cells come into contact with another specific subset of microRNAs, they are able to suppress genes that promote cell growth. Therefore, by normalizing the function of defective microRNAs in cancer cells, it will be possible to reverse aberrant growths, reprogramming cancer cells to normal cells). The pending task here is to identify adhesion proteins that interact with microRNAs, which ultimately orchestrate complete cellular programs of simultaneous regulatory expression of a group of genes. Experimentally Anastasiadis has given microRNAs back into the cancer cells and get them effectively reprogrammed to normal cells. Anastasiadis concludes that anomalous forms of cells identified by some histopathologists as cancer, are rather the expression of defective intercellular adhesions, not always being evidence of malignancy. III) Finally, there is currently great progress in the rapid identification of traces of tumors in small blood samples containing abnormal DNA, having created the 2016, the Grail company, with a capital of $ 1.5bn of dollars with contributions from Microsoft and Amazon to detect multiple cancers at once, before the onset of symptoms. The identified fragments known as circulating tumor DNA (ctDNA) are eliminated by cancer cells. Although there are still problems of sensitivity and specificity, high costs and quantity of blood to be extracted, the goal is to detect 10 cancers at a time. There is confidence because it is now possible to scan very small fragments and identify those few with alterations that may indicate cancer. Other companies on this flight are Epigenomic, Guardant Health, Breathomics, Owlstone Medical.

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Saturday, February 16, 2019



In an updated paper about the current possibilities of creating a functional artificial brain similar to the human brain, the weaknesses and strengths of such efforts are exposed in Science. The analysis begins with the question: ¿Can  machines think?, self-answered by Alan Turing, in 1950, who alluded  to: a) A mathematical negative formulated by Kurt Gödel and Church, Kleene, Boater and Turing, referred to digital computers, which despite having an infinite capacity, such machines in front of certain things or questions would not respond or would do it wrongly. Penrose suggested (1980,1994), that certain human brain molecular structures could adopt a state of quantum superposition and entanglement (more so, if electrons transiting through human neural circuits do so in ionic forms), paving the way for the future use of artificial quantum computers in order to equal human brain performance. b) Returning to Turing, he closed his point of view   postulating by that time (1950), that for machines to start thinking like a human brain the important thing was to imitate the complex biological neural computational systems, having as a guide the human neural circuits, imitation that to the present time  has made possible the creation of neural circuits systems imitating the cerebral cortex (deep network), constructed with successive layers of elements similar to neurons connected by artificial synapses, producing speech recognitions, complex games, translation of texts, computer vision, classification and segmentation of objects, capture of images, where someone  try to produce a short verbal description of an image, answer visual questions and of human communication about the content of an image, or non-visual tasks: analyze humor and sarcasm, comprehension and intuitive aspects of social things, serve as assistants persons, in medical diagnosis, automatic car handling. Despite this, there are problems to be solved: I) To improve the adjustment of learning through synapses to produce desired output patterns, conditioned by training at the inputs. II) Achieve a learning with deep artificial neural networks that go beyond simple memorization producing logical outputs, not necessarily programmed during the learning process. III) In this perspective, highlights the incorporation into AI (artificial intelligence), associated to deep neural artificial networks of the called: learning reinforcement: LR (mapping of situations or actions to maximize the signal of reward or reinforcement, not to take certain actions, but to discover through trial, error and reward the best option in order to modify the behavior). LR models combined with AI algorithms, are currently applied to video games, Go and Chess, reaching in this last level of world champions with only 4 hours of training. IV) However, the most notable differences between the biological circuit and artificial neural networks systems are those based on structure: biological neurons are complex and diverse in morphology, physiology and neurochemistry. The entrances to excitatory pyramidal neurons are distributed over very complex dendritic branches. Cortical inhibitory neurons exhibit different functions, none of these heterogeneities being included in artificial neural networks.  Biological cerebral cortical circuits are more complex than models of artificial neural networks, including lateral connectivity between neurons, as well as local connections, more extensive connections and connections up and down in the hierarchical cortical regions. V) It is expected that artificial neural networks will promote a real human understanding, in order to address broad aspects of cognition and general artificial intelligence (IGA). Meanwhile, these techniques continue to be perfected under the guidance of neuroscience. VI) There are other functional differences between biological and artificial systems: A) AI's current artificial models rest heavily on the empirical side using simple and uniform artificial neural network structures employing large sets of training data for learning. Biological systems carry out tasks with limited training, learning about pre-existing network structures already encoded in circuits before learning, with which insects, fish and pigeons, perform complex navigation tasks using part of an elaborate set of innate mechanisms with sophisticated computational capabilities. B) Therefore, the development of complex cognitive and perceptual activities in children with little training, in the first months of their lives is possible, recognizing they complex instruments such as their hands, following people with their gazes  and distinguish visually if the characteristics of certain animals are dangerous or not, while developing an incipient understanding of physical or social interactions, through unsupervised learning, given the presence of innate cognitive systems generated by evolution, which facilitated the acquisition of significant concepts and skills. Recent models of visual learning in childhood, show that significant and complex concepts are not innate or learned by the child, but are proto-concepts that provide signals of internal teaching guiding the learning system along pathways that lead to a progressive acquisition and organization of complex concepts with little or no explicit training. Sometimes, a particular pattern of moving images provides an internal signal for the recognition of their hands that helps them to manipulate objects guiding the learning system in the direction of their gaze. Innate structures implemented in cortical regions with specified connectivity warn initially of specific input errors. Perhaps in the future, these pre-existing structures could be coupled to artificial neural models to simulate human learning. Imagine computational learning methods starting from proto-concepts with structures inserted in humans or robots that learn to quickly become familiar with unknown environments in an efficient and flexible way, very different from the current learning procedures. Summing up: I) Following Shimon Ullman, we believe that each machine or robot of the future that must possess a human-like brain should it be  virginally inserted a basic code -not to obey commands- but to complete what it should be (or do), before each new situation or environment, using analogies, logic or emerging thinking solutions. II) Today's hyper-super-computers have no future for this purpose because: a') They use artificial neuron-artificial neuron transmissions, using electrons that circulate through metallic means. a'') Human neurons send messages to other neurons using ions that are more adapted to quantum models (entanglements and others), allowing almost simultaneous transmissions in all possible planes, including feedback-type ones. Although current computers transmit information in several planes, they lack artificial-totalizing organizer neurons, which to be functional should be spherical or pyramidal. b') Although the human brain allows the circulation of 20% of total human body blood at every moment, it does not get very hot, because the membranes of its neurons are covered by a fat content resistor that allows them to capture electrons from the environment and adapt them to ions that allow the transmission of the message to millions of other neurons . b'') You have to copy this resistor model and include it in a quantum computer. c') The problem of reduced space of the artificial brain is solved in quantum computers using artificial neurons with spherical or pyramidal shapes capable (or almost) of dealing with thousands or millions of other artificial neurons.

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Sunday, February 10, 2019



1) Identify problems, choose the main problem (the one that explains the rest). Develop a hypothesis and test it. 2) Analyze the results, assessing the evidence. 3) New and improved hypothesis. Questions and cross-questions to the maximum. Incorporation of contributions and ideas from other scientific areas. New hypothesis and so on, again and again. Let's see 2 cases:

I) First case. Recognized as an interstellar object ("Oumuamua"), by the International Astronomical Union, this entity discovered in October 2017, supposedly natural, has been questioned by the astronomer Abraham Loeb (Harvard University, Astronomy Deparment), given the pretty bizarre features of this, to be an asteroid or a comet of our solar system : a) when passing close to the earth the entity rotated on itself  every 8 hours b) it has a brightness that changes by a factor of 10 c) It  is 5 times longer than wide d) It does not emit much heat e) It  is a good reflector of light, but poorly conservative of it f) It  does not absorb light well g) not much is known about its surface because it has not been possible to take a clear close image of its surface h) The entity  follows a path not governed by the sun's gravity  j) The entity  exhibits an extra force that drives it. Such kind of an object with unusual characteristics and limited data,  has forced Loeb to propose several alternatives for it,   instead of accepting it exclusively as a natural entity, once again dividing an elite of world scientists: a minority that argues that possessing  such unusual features, one possibility  -within several- is that it is an extraterrestrial probe launched by an intelligent civilization, and another represented by scientists not so open minded to ensure that it is a natural object and nothing more. An interdict on the very frontier of science, whose arguments as always bring us back to Plato and dialectics and whose final solution will once again be determined by the evidence. A phenomenon like this with such unusual characteristics can be anything, until it is proven otherwise. 

From quantamagazine

II) The other case corresponds to the enigmatic subject of matter and dark energy and black holes, conforming 95% of our visible universe, for which the theoretical astrophysics of Yale University: Priyamvada Natarajan, has proposed to study them coherently, using various methods including the mapping of them. The central theoretical idea of the ​​Indian astrophysics is to suppose that supermassive black holes are a fundamental part of the structure, the energetic area and the evolution of our universe, due to being located in the center of galaxies, determining the form and other characteristics of the galaxies. A very difficult subject to study located on the same frontier of science. How to demonstrate this theory if the mentioned phenomena are characterized by their invisibility. To face this challenge, the Natarajan, argues to have the stamina, mental clarity and above all know what she is   doing aided by mathematical approaches, quite necessary in this case. 1) She says that one of the first things to do is to understand why all the current physics collapses when we are close to a black hole? auto answering she says:” you need a lot of mathematical support”. 2) She  also argues that there is a certain relationship or interweaving between the mass of the stars in the central part of the galaxies and the central area of ​​the black holes that host them, needing here approximations of theoretical physics, to know how to join the framework  and early growths of these phenomena, simultaneously. 3) Regarding the mapping, the Natarajan argues that the Hubble telescope has provided incredible images to map and analyze dark matter, offering indirect images of this matter when estimating the extension and curvature of the light emitted by different distant galaxies. Adding: "But, not everything depends on the Hubble images. My maps need mathematical orientations especially when small clumps of dark matter are identified, which correlated perfectly with the cold, non-interactive dark matter". In images taken by the Hubble telescope of the cluster galaxy: MACS0416 and of the same image with superimposed blue distribution of dark matter, inferred from the distortion of light from distant galaxies (the background ones), it is observed how supermassive black holes could have initially formed at the center of galaxies. The scientist hopes that with larger telescopes it will be possible to identify bright quasars (luminous galactic centers), supplied by billions of solar masses and black holes, when the universe was only 10% of its current age. Part of the above is supported by the Natarajan in 2 articles (2005/2006), in which when lecturing on "massive seeds",  she raised the extraordinary idea that it is possible to bypass the formation of a star forming instead  seeds  of massive black holes, of approximately 10,000 to 1 million times the mass of the sun, able to explain the emergence of quasars in very early times of our universe. 4) On the spiral forms of galaxies, Natarajan argues:In the same way that when you pull the plug of the bathtub, the water forms a vortex, something similar happens in the early universe with the formation of gas discs, quickly positioning the gas siphon in the center". This direct collapse of "seeds of black holes" is part of a larger cosmic evolutionary history, subsequent to the generation of a population of black holes: how they form, evolve, turn into quasars, go out and shine until today. 5) The idea of ​​the direct collapse of supermassive black holes (quasars of early times of the universe, fed by supermassive black holes), is today, a leading theory in the formation of the early universe, consensuated by successive small pieces of evidence. To support the above, in a computer simulation, the Natarajan successfully programmed the direct collapse of black holes: populated the early universe and propagated the growth of these galaxies until today. 6) The noted Hindu scientist, hopes that the James Webb Space telescope to be launched in 2021, observe the space deeply and backward in time, paying attention to the formation of galaxies of the early universe, facts that will test the idea of early collapse of the Natarajan. The James Webb telescope is able to see images of dark matter more accurately. Premonitorily, the Natarajan affirms that if the new telescope identifies quasars in the first epochs of the universe, these will have to be black holes with direct collapse.


1) Identificar problemas. Escoger el problema principal (el que explique al   resto de problemas). Elaborar una hipótesis y someterla a prueba. 2) Analizar los resultados, valorando las evidencias. 3) Nueva hipótesis, mejorada. Preguntas y repreguntas al máximo. Incorporación de aportes e ideas de otras áreas científicas. Nueva hipótesis y así, una y otra vez.  Veamos 2 casos:

I) Reconocido como un objeto interestelar (“Oumuamua”), por la International Astronomical Union, este ente descubierto en octubre del 2017, supuestamente natural, ha sido  puesto en entredicho por el astrónomo Abraham Loeb (Harvard University, Astronomy Deparment), dadas   las  características bastante bizarras de  este,  para ser un asteroide o un cometa de nuestro  sistema solar,  el mismo  que : a) al pasar   cerca de la tierra rotaba sobre si cada 8 horas b) que posee una brillantez que cambia por un factor de 10 c)  que es 5 veces más largo que  ancho d) que no emite mucho calor e) que es un buen reflector de la luz, pero mal conservador de ella f) que no absorbe  bien la luz g) del que no se conoce  mucho sobre su  superficie porque no se ha podido   tomarle una imagen cercana nítida h) que sigue un trayecto no gobernado por la gravedad del sol i) que no es influenciado por la gravedad del sol   j)  que exhibe una fuerza extra que lo impulsa. Un objeto con características desusuales y  con  datos limitados,  que ha obligado a  Loeb a  proponer  para este objeto  varias alternativas, en vez de aceptarlo exclusivamente como un ente natural,  dividiendo una vez más a una elite de   científicos mundiales: una minoría que arguye  que al poseer  características tan  desusuales, una posibilidad dentro de varias, es que  se trate de una  sonda extraterrestre lanzada por una civilización inteligente y,  otra representada por científicos de  mente no tan abierta que  aseguran que se trata de un objeto natural y nada más. Un entredicho en la misma frontera de la ciencia, cuyos argumentos como siempre nos retrotraen a Platón y a los dialecticos y cuya solución final una vez más será determinada por las evidencias. Un fenómeno como este con características tan desusuales puede ser cualquier cosa, hasta que no se demuestre lo contrario, claro. II) El otro caso corresponde al enigmático asunto de la materia y energía oscura y los agujeros negros, conformantes del 95% de nuestro universo visible, para los cuales la astrofísica teórica de la Universidad de Yale:  Priyamvada Natarajan, se ha propuesto   estudiarlos coherentemente, empleando diversos métodos incluyendo el mapeo de los mismos.  La idea teórica central de la astrofísica hindú es suponer que agujeros negros supermasivos son parte fundamental de la estructura, el área energética y la evolución de nuestro universo, en razón de estar   ubicados en el centro de las galaxias, determinando la forma y otras características de las galaxias.  Un tema bastante difícil de estudiar ubicado en la misma frontera de la ciencia. Como demostrar esta teoría si los fenómenos mencionados se caracterizan por su invisibilidad. Para afrontar este desafío, la Natarajan, arguye disponer de la estamina, la claridad mental y sobre todo saber lo que está haciendo ayudada por aproximaciones matemáticas bastante   necesarias en este caso. 1) Dice ella, que una de las primeras cosas por hacer es entender ¿porque toda la física actual colapsa cuando estamos cerca de un agujero negro?, auto respondiéndose que, para responder a esta pregunta, necesita mucho soporte matemático. 2) Arguye, asimismo, que existe cierta relación o entrelazamiento, entre la masa de las estrellas en la parte central de las galaxias y el área central de los agujeros negros que las hospedan, necesitando aquí aproximaciones de física teórica, para saber cómo   unir estructural y tempranamente los crecimientos de estos fenómenos, en forma simultánea. 3) Respecto al mapeo, la Natarajan arguye que el telescopio Hubble, ha proporcionado imágenes increíbles para mapear y analizar la materia oscura, ofreciendo imágenes indirectas de esta materia al estimar la extensión y curvatura de la luz emitida por diferentes galaxias lejanas. Agregando: “No todo depende de las imágenes del Hubble, mis mapas necesitan orientaciones matemáticas especialmente cuando se identifican grupos pequeños de materia oscura, correlacionados a la perfección con la materia oscura fría, de tipo no interactivo”. En imágenes tomadas por el telescopio Hubble de la galaxia en racimo: MACS0416 y de la misma imagen con distribución en azul superpuesta de la materia oscura, inferida a partir de la distorsión de la luz procedente de galaxias distantes (las de fondo), se observa como podrían haberse formado inicialmente agujeros negros supermasivos en el centro de las galaxias. La científica espera que con telescopios más grandes se logren identificar cuásares brillantes (centros galácticos luminosos), abastecidos por billones de masas solares y agujeros negros, cuando el universo tenía apenas un   10 % de su edad actual.  Parte de lo anterior es sustentado por la Natarajan en 2 artículos (2005/2006), en los que al disertar sobre “semillas masivas”, plantea la extraordinaria idea de que es posible  bypasear la formación de una estrella formando en su lugar  semillas de  agujeros negros masivos, de aproximadamente   10,000 a 1 millón de veces la masa del sol, capaces de explicar la emergencia de cuásares en épocas muy tempranas de nuestro universo. 4) Sobre las formas espirales de las galaxias, la Natarajan arguye:” Del mismo modo que cuando usted jala el tapón de la bañera, el agua forma un vórtice, algo similar sucede en el universo temprano con la formación de discos de gas, posicionándose rápidamente el sifón de gas en el centro”. Este   colapso directo de “semillas de agujeros negros” es parte de un historial evolutivo cósmico más grande, subsiguiente a la generación de una población de agujeros negros:  se forman, evolucionan, se tornan en cuásares, se apagan y brillan hasta hoy.  5) La idea del colapso directo de los agujeros negros supermasivos (cuásares de épocas tempranas del universo, alimentadas por agujeros negros supermasivos), es hoy, una teoría líder en la formación del universo temprano, consensuada por   sucesivos pequeños trozos de evidencia. Para sustentar lo anterior, en una simulación en computadora, la Natarajan programo el colapso directo de agujeros negros, pobló el universo temprano y propago el crecimiento de estas galaxias hasta hoy. 6)  La notable científica hindu, espera que el telescopio James Webb Space a ser lanzado el 2021, observe     el espacio profundamente y hacia atrás en el tiempo, prestando atención a la formación de galaxias del universo temprano, hechos que pondrán a prueba la idea del colapso   temprano de la Natarajan. El telescopio James Webb está capacitado para ver imágenes de la materia oscura con mayor precisión. Premonitoriamente, la Natarajan afirma que el nuevo telescopio identifica   cuásares en las primeras épocas del universo, estos tendrán que ser   agujeros negros con   colapso directo. 

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Saturday, February 02, 2019



Some scientists believe that the final objectives of the development of Artificial Intelligence (A.I.) -and of robotic brains- never reach the tessitura, sophistication and creativity of human brains. Faced with this challenge, the continuous discoveries in A.I. and neuroscience contradict the previous, indicating that feelings, passions, ideas, memory and all abstract concepts are computable. Thus, in the mentioned fields, it is important to analyze several studies related to navigation and memory made in animals and humans’ brain, performed by notable scientists, some of them winners of the 2014 Nobel Prize in Medicine: John O Keefe (University College/London), May-Britt Moser and Edvard Moser/University of Oslo, Norway) and others. Moreover, Jeff Hawkins (Numenta/University of California), is clearly convinced that these new discoveries will soon be applied to the improvement of A.I., expecting to have in the near future robots with autonomous memory and navigation (to climb, to fly, to walk, to run), systems. We are talking about brain’s codes of network navigation (grid codes), to be implanted in robots, making them more flexible, creative, and powerful. The evidence related to internal brain’s grid codes sustain that these   codify abstract knowledge using spatial representations, similar to how physical spaces are mapped. According to Kim Stachenfeld (Deep Mind), memory and navigation have a physical base located in the hippocampus, being essential the grid codes for both. Grid-codes are a form of spatial brain representation, which helps to navigate through visual, sonic and other areas full of abstract concepts. In this regard, 2 types of cells have been discovered in the hippocampus: a) one related to two-dimensional (2D) space navigation and b) one related to memory. John O'Keefe discovered in rat brains: place cells (position cells/their visible discharges indicate their current specific position), which together create a total spatial map. By changing the experimental rats from place, the place cells perform re-mapping. The hippocampus remaps the area, creating and storing cognitive maps. For their part: May-Britt Moser and Edvard Moser, studied the entorhino cortex, the first to deteriorate (navigation and memory areas), in Alzheimer's disease. For Mosers the grid code is a kind of metric or coordinate system, useful for physical and abstract brain navigation, having been found in humans (using f-MRI), an hexadirectional signal, with properties emanated from grid codes, previously identified in physical navigation tasks. Jacob Bellmund (Max Planck Institute/Leipzig and Kavli Institute for Systems Neuroscience/Norway), believes that unlike place cells, grid cells do not represent particular locations, but a navigation system independent of the location (brain’s GPS). Each grid cell fire in regularly spaced positions forming a hexagonal pattern, of the same size, in which each hexagon has 6 equilateral triangles. Grid cell fire each time the subject reaches the vertex of any of these triangles. There are grid cells with larger or smaller hexagons and together they are able to map any special position of the environment and any particular location. Grid cells are responsible for the call: integration path. The grid network not only sends a lot, but different types of information. György Buzsaki (NY, University School of Medicine), says that the hexagonal system is a solution to represent any structured relationship, from word meaning maps to maps of future plans. In addition, the hippocampus contains place cells that sometimes behave as "time cells" activating in certain situations successive moments of time, rather than successive positions in space. When the rats remain in a constant place, the cells are activated in the hippocampus to trace their temporal progression -in this case- some neurons are activated in the first seconds and others shortly, thereafter, acting the time as a different dimension in this equation; evidence of a codified system that only represents time in the context of memories or experiences. On the other hand, scientists from Princeton University discovered another potential dimension: sound, which monitored brain activity in rats that pushed a lever to change the frequency of one tone to adapt it to another that they had previously heard. The most recent news regarding grid codes, argue that all physical and abstract knowledge can be plotted with these codes. Recently other cells have been identified that help  the grid system: 1) head direction cells that discharge when an animal points its head in a particular direction 2) speed cells that indicate the frequency at which the subjects move in the space 3) boundary cells that represent the location of objects or people on the walls or edges not only of the physical space but also on the edges of separate conceptual events mobilized in temporal sequences. It is known that the grid code is less stable in the elderly, with locations in these people not so efficient. It remains to be seen how grid cells work in flying bats that navigate in 3D. Sure, the future is challenging in this field.

Algunos científicos creen que los objetivos finales del desarrollo de la Inteligencia Artificial (A.I.) y -de los cerebros robóticos- nunca alcanzaran la tesitura, sofisticación y creatividad de los cerebros humanos. Frente a este desafío, los continuos descubrimientos   en A.I. y neurociencia contradicen lo anterior, indicando que   sentimientos, pasiones, ideas, la memoria y todo lo abstracto son procesos computables, robotizables. De modo que en los campos mencionados,   importa  analizar  y relacionar prospectivamente diversos estudios relacionados con la navegación y la memoria  cerebral animal y humana,   realizados  por notables científicos, algunos de ellos ganadores del Premio Nobel de Medicina 2014 : John O Keefe (University College/London), May-Britt Moser and Edvard Moser/Universidad de Oslo, Noruega)   y, otros. Es más, Jeff Hawkins (Numenta/University of California), tiene el claro convencimiento de que estos nuevos descubrimientos pronto serán aplicados al mejoramiento de la A.I. Estamos hablando de códigos de navegación en red (grid codes), cerebrales a ser implantados pronto en robots, tornándolos más flexibles, creativos, y poderosos al unificarse las funciones de memoria y navegación. Los estudios referidos avalan la eficiencia cerebral para codificar conocimientos abstractos empleando representaciones espaciales, de forma similar a como se mapean los   espacios físicos. Según      Kim Stachenfeld (Deep Mind), la memoria y la navegación tienen una base física localizada en el hipocampo, siendo los grid codes esenciales para ambas. Los    grid-codes son una forma de representación cerebral espacial, que ayuda a navegar por áreas visuales, sónicas   y otras plenas de conceptos   abstractos. Al respecto se han descubierto    en el hipocampo 2 tipos de células: a) una relacionada con la navegación espacial bidimensional (2D) y b) otra relacionada con la memoria.  John O’Keefe descubrió en cerebros de ratas: place cells (células de posición/descargas visibles indican su posición especifica actual), las que juntas crean un mapa espacial total. Al cambiar las ratas de experimentación de lugar, sus place cells realizan   re-mapeo. El hipocampo remapea el área, creando y almacenando mapas cognitivos.  Por su lado:  May-Britt Moser y Edvard Moser, estudiaron el córtex entorrino, el primero en deteriorarse (áreas de    navegación y   memoria), en la enfermedad de Alzheimer. Para los Mosers el código grid es una especie de sistema métrico o de coordenadas, útil para la navegación cerebral física y abstracta, habiéndose encontrado en monos: mezclas de    navegación activa del espacio visual y, en humanos (mediante f(MRI), una señal hexadirectional, con propiedades emanadas de   códigos grid, previamente identificados en tareas de navegación física. Jacob Bellmund (Max Planck Institute/Leipzig y The Kavli Institute for Systems Neuroscience/Norway), opina que a diferencia de las place cells, las grid cells no representan localizaciones particulares; sino un sistema de navegación independiente de la localización (GPS cerebral) Cada célula grid descarga en posiciones regularmente espaciadas formando un patrón hexagonal, del mismo tamaño, en el que cada hexágono tiene 6 triángulos equiláteros.  A medida que uno camina, una célula grid descarga cada vez que el sujeto   alcanza el vértice de cualquiera de estos triángulos.  Existen conjuntos de grid cells con hexágonos más grandes o pequeños y juntas son capaces de   mapear cualquier posición especial del medio ambiente y cualquier localización particular.  las células grid son las responsables de la llamada: vía de integración.    La red grid, no solo envía mucha, sino diferentes tipos de información. György Buzsaki (NY, University School of Medicine), dice que el sistema hexagonal es una solución para representar cualquier relación estructurada, desde mapas de significados de palabras hasta mapas de planes futuros. Además, el   hipocampo   contiene   place cells que a veces se comportan como “time cells” activándose en ciertas situaciones   indicando momentos sucesivos del tiempo, más que posiciones sucesivas en el espacio). Cuando las ratas permanecen en un lugar en forma constante, las células se activan en el hipocampo para trazar su progresión temporal: algunas neuronas se activan en los primeros segundos y otras poco después, actuando el tiempo como una dimensión diferente en esta ecuación; evidencia de un sistema codificado que únicamente representa al tiempo en el contexto de memorias o experiencias.  De otro lado, en la   Princeton University se descubrió otra dimensión potencial: el sonido, que monitoreaba la actividad cerebral en ratas que empujaban una palanca para cambiar la frecuencia de un tono para adecuarlo a otro   que ellas habían escuchado previamente. Las noticias más recientes respecto a los códigos grid, sostienen que todo el conocimiento físico y abstracto, puede ser ploteado con estos códigos.  Recientemente se han identificado otras células  que ayudan en el sistema grid:  1) las  células head direction   que descargan cuando un animal apunta su cabeza  en una dirección  particular 2) las  células speed que  que indican  la frecuencia a la cual se mueven los sujetos en el   espacio 3) las   células boundary que representan la  localización  de objetos o personas en   las paredes o bordes no solo del  espacio físico  sino también en los bordes de eventos conceptuales separados movilizándose en secuencias temporales.  Aunque faltan estudiar funciones adicionales de este sistema, se sabe que el código grid es menos estable en ancianos, con localizaciones en estas personas no tan eficientes.  Queda por ver como trabajarían las grid cells en murciélagos volando, que navegan en 3D.  

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