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The Future of the Brain: Essays by the World's Leading Neuroscientists Formato Kindle

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Including a chapter by 2014 Nobel laureates May-Britt Moser and Edvard Moser

An unprecedented look at the quest to unravel the mysteries of the human brain, The Future of the Brain takes readers to the absolute frontiers of science. Original essays by leading researchers such as Christof Koch, George Church, Olaf Sporns, and May-Britt and Edvard Moser describe the spectacular technological advances that will enable us to map the more than eighty-five billion neurons in the brain, as well as the challenges that lie ahead in understanding the anticipated deluge of data and the prospects for building working simulations of the human brain. A must-read for anyone trying to understand ambitious new research programs such as the Obama administration's BRAIN Initiative and the European Union's Human Brain Project, The Future of the Brain sheds light on the breathtaking implications of brain science for medicine, psychiatry, and even human consciousness itself.

Contributors include: Misha Ahrens, Ned Block, Matteo Carandini, George Church, John Donoghue, Chris Eliasmith, Simon Fisher, Mike Hawrylycz, Sean Hill, Christof Koch, Leah Krubitzer, Michel Maharbiz, Kevin Mitchell, Edvard Moser, May-Britt Moser, David Poeppel, Krishna Shenoy, Olaf Sporns, Anthony Zador.

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  • Formato: Formato Kindle
  • Dimensioni file: 3956 KB
  • Lunghezza stampa: 283
  • Editore: Princeton University Press (24 novembre 2014)
  • Venduto da: Amazon Media EU S.à r.l.
  • Lingua: Inglese
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  • Posizione nella classifica Bestseller di Amazon: #166.429 a pagamento nel Kindle Store (Visualizza i Top 100 a pagamento nella categoria Kindle Store)
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Le recensioni clienti più utili su (beta) (Potrebbero essere presenti recensioni del programma "Early Reviewer Rewards") 4.0 su 5 stelle 29 recensioni
16 di 16 persone hanno trovato utile la seguente recensione
4.0 su 5 stelle A very mixed bag of essays by neurobiological big bosses and stars. 30 aprile 2015
Di Greenrat - Pubblicato su
Formato: Formato Kindle Acquisto verificato
As everybody predicts that the future holds a similar blooming of neuroscience as we saw in genomics, a book about its future, written by the biggest names in the field should be an exciting read, right? Sadly not so much, and maybe it reflects the issues of the field. Almost half of the essays are layed out according to the same plan: there is an [awesome method]; there is 86 billion neurons (this number is present in at least half of the essays in the book); we would use the [awesome method] to map/simulate/sequence the human/mouse brain with the billions dollars that DARPA/Paul Allen/European Commission/Obama gave us, generating peta/zetabytes of data. It's not like these projects would not give us a lot of insight of how brain works, but some of the articles look more like a Soviet 5 year plan, with the projected yields of neurons and axons mapped instead of tons of steel (the essay from Allen institute talks about SOPs, QC and web interfaces for the brain map, much needed stuff, but not really visionary).It's of course not all like that - a lot of ideas, as for example Church's "Rosetta Brain/BOINC" or Maharbiz's "Neural Dust" are visionary and showing that we could hope for new revolutions in methodology. In my opinion the best part of the books comes from the sceptics of the "Big Projects", especially pieces from Ned Block, Matteo Carandini, and Gary Marcus, which argue that we should focus on ideas and theories and experiments to test them, rather than hope that large quantities of different data would give them to us on a silver platter. In the end it's maybe telling that the cute sci-fi afterword imagining the state of neurobiology in 2064, predicts it's crisis after 2020s.
6 di 6 persone hanno trovato utile la seguente recensione
4.0 su 5 stelle A Glimpse into the Current State of the Field of Neuroscience 11 settembre 2015
Di Raj - Pubblicato su
Formato: Formato Kindle Acquisto verificato
This book isn't quite what I was expecting. I thought it would be more like "This Explains Everything": A collection of essays written by scientific experts, but written for the casual reader. The audience for this book is unclear to me. On the one hand, it seems like the essays are written for others in the neuroscience field, and are assertions of where the field needs to go and what needs to happen next. On the other hand, the essays assume no knowledge about the neuroscience field and each essay provides an overlapping overview of recent or notable works and achievements.

The essays can be a bit on the dry side.

In a nutshell, I would say this is book is an excellent insight into the *field* of neuroscience, who the major players are, and what the next steps are. The ideal reader might be a scientist that is interested in neuroscience but is not already in the field. I wouldn't recommend this to a casual reader who only wants to learn more about how the brain works.
1 di 1 persone hanno trovato utile la seguente recensione
1.0 su 5 stelle Not sure who will benefit from this book 31 marzo 2017
Di David T. Yu - Pubblicato su
Formato: Copertina rigida Acquisto verificato
This book contains a number of essays probably by leading investigators in the field of neuroscience. However, the book neither provides introduction into the field, nor provides hard data, nor any hypotheses or theories of neuroscience. In stead, each essay projects what the authors would predict into the future about the development of techniques of investigation. The book is useless for those not familiar with neuroscience. It is also useless if you are already in the field of neuroscience. I think the authors of the various essays are simply writing to each other.
61 di 68 persone hanno trovato utile la seguente recensione
5.0 su 5 stelle A Big Vision of a Long Future of a Lot of Work 27 novembre 2014
Di Stephen E. Robbins - Pubblicato su
Formato: Formato Kindle Acquisto verificato
This book is a great antidote. It is not the best antidote for it still lacks vision as to the actual depth of the problems being faced, but there is enough vision and awareness, particularly of the true state of affairs in neuroscience, to pack this antidote-pill with plenty of power nevertheless. The ill for which the pill is the antidote is the vastly optimistic speculation and over-worry of the AI community - the supposed very soon equivalence of AIs to human intelligence once we have (again, very soon) simulated the human brain, the anxiety over robot takeovers of the human race and over the robot/AI's lack of "values" as opposed to their soon-to-be massive amped-up intelligence. The list is long: Kaku (The Future of the Mind), Barrat (Our Final Invention), Armstrong (Smarter Than Us), Muehlhauser (Facing the Intelligence Explosion), Kurzweil (How to Build a Mind) , and many more. This book makes them look, well, questionable at best.

Marcus and Freeman, the editors/contributors, set the tone in the intro: A brain with over 85 billion neurons, where there are perhaps 1000 different neuron types, each with different physical and electrical characteristics, each with functions of which we know nothing about. Overarching this already vast scope of discovery: "...we have yet to discover many of the organizing principles that govern all that complexity...we are still shaky on fundamentals like how the brain stores memories..." And worse, "...all agree that the most foundational properties of neural computation have yet to be discovered." On the deck are huge initiatives - the Obama BRAIN initiative, the European human brain project and more - and new techniques and methods - optical tracing in neurons, genetic techniques, the ability to record thousands of neurons simultaneously and more. Many of the contributors discuss these new initiatives and technologies, the rate of progress they envision, the obstacles, the limitations. The others are focused on the deep and massive problem the initiatives and new technologies both engender and face: Confronted with an enormous mass of neural data re connections, firings, frequencies, response strength, etc., perhaps on the order of zetabytes for even short recordings of brain function, how does one discover within this data the organizing principles governing the brain? How, as Shenoy notes, do we avoid "drowning in the data?"

The problem is enormous. As one contributor illustrates it, it is like trying to understand how a laptop computer functions via tracing its connections and modeling these over time - when we are not even aware of the existence of something called software! Shenoy's reliance on "levels of abstraction" for analysis (where for a computer, software is one "level") sounds nice, but the role of "software" in the case of the brain is in our correct understanding of, or theory of, firstly, perception, i.e., an understanding of the origin of the image of the external world (our experience) to include its "qualia." This (stated in terms of the origin of our image of the external world) is the more correct statement of the hard problem - Chalmers' version, stated only in terms of the origin of "qualia," has been misleading. It is foundational; it is a current mystery. Yet without an understanding of perception (experience), we cannot begin to have a theory of memory, i.e., of the "storage" of this experience, IF experience is even stored in the brain - and this theoretical chasm is why we are "still shaky" on this fundamental, namely the storing of memories. This understanding is a must-have to guide our analysis of the mass of neural data to come. This in turn cascades into how cognition, thought and language work (as all is based upon this experience and its retrieval) and beyond. The lack of this appreciation hides in areas of the book. Eliasmith pictures his "Spaun" neural model of the brain with arrow-connected boxes - visual input, information encoding, transform calcs, reward evaluation, action selection, action output. Nowhere is there a clue how the goings-on in the boxes become my image of the external world - watching my hand stirring a cup of coffee in the kitchen - yes, my experience. And endemic to neuroscience - nowhere is there an acknowledgement that in perceiving such an event, the neural mass is in fact responding to a mass of environmental information involving invariance laws - radial flow fields over the coffee surface, adiabatic invariants (a ratio of energy of oscillation to frequency) in the periodic motion of the spoon existing over haptic flows, texture gradients supporting size constancy, inertial tensors defining the wielding of the spoon, flow fields defining even the cup's form, and on, and all comprising a prior level of theoretical effort (still vastly incomplete) essential to making any sense of the neural data, and all yet irrelevant, as far as I have ever been able to discover in the literature, to the neuroscientists.

Describing the depth of this theoretical problem (the real stand-in for the "software" problem) is a weakness in the book. But enough hints are there. Freeman notes that the function of V2 (a visual area), despite massive data analysis, has resisted understanding for years (along with limited grasp of V3, V4, V5, etc.). Only by making a sharp theoretical guess - note, theoretical - has some partial progress for V2 recently been made. The more complex, the more massive the data, the more theory drives data analysis. And we are in a crisis of theory. Hints of the symptoms surface in the above discussion where it is noted that great supposed progress was made by Hubel and Wiesel's 1959 discovery of cells in V1 which are sensitive to the orientation and direction of lines, seeming to give the basis for the parsing of a visual scene - stirring the coffee - but the expectation of finding the logical extensions of such processing in higher visual areas has never come to fruition. In truth, perception theory itself has recognized that elements such as Hubel and Wiesel's cannot be the basis for scene recognition (see On Time, Memory and Dynamic Form, in Consciousness and Cognition (journal), 2004), i.e., one of the hitherto very basic neural assumptions about our perception of the external world is itself, well, shaky. But this only brings us back to the magnitude of the theoretical problem which precedes the analysis of a mass of neural data.

In all, however, this is a book of great interest, thought provoking, very informative on neuroscience today, on discoveries made, and action to come. Marcus' unbridled trashing of the current cognitive science neuro-favorite, namely the connectionist network (on which the mythological hopes of current AI are based), is a refreshing change. Something else, some other form of computation, as he argues, is needed. This is the great question, at least broached in the book to some degree - what is the form of computation that the brain is actually employing??? It is not what we think today. (Turing himself allowed for a "broad computation" which is not the form of computation embodied in computers or connectionist networks (same thing) or Turing Machines). Just to glimpse how different things might be, what if, as Bergson presciently envisioned (Matter and Memory, 1896), the brain's dynamics actually supports a form of modulated reconstructive wave passing through a universal, holographic field, specifying a subset of the field as an image of the external environment? Brain-computation would indeed be quite different. The final chapter, which takes us on a future look to 2064 is interesting in its assessment of the hurdles. The breakthrough waits until 2064; at least we are beyond Kurzweil's "Singularity" of 2045. But no, the hard problem will not simply "resolve," like "what is life" as they propose. It is, again, foundational. Even in the candor of this final vision, then, I think the work that remains is deeply underestimated.
1 di 1 persone hanno trovato utile la seguente recensione
4.0 su 5 stelle Very interesting for some one who wants to know the recent research of the brain. Can be too technical. 4 maggio 2016
Di JoseY - Pubblicato su
Formato: Copertina rigida Acquisto verificato
It's a collection of semi-scientific papers. Some are better written, that is sticking at the topic, better than others. Quite technical. I am an MD. Some term and concepts are difficult for me.
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