Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain
- Downloads：5806
- Type：Epub+TxT+PDF+Mobi
- Create Date：2021-05-23 09:19:12
- Update Date：2025-09-06
- Status：finish
- Author：Grace Lindsay
- ISBN：1472966422
- Environment：PC/Android/iPhone/iPad/Kindle
Reviews
Rhys Lindmark
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This was a good， clear overview on how computer science/mathematical thinking has co-evolved with neurobiology。 I like it because it gives a meta-view on Thousand Brains and Predictive Processing instead of arguing for them directly。It was too long for me though。 I didn't find much value in the personal historical stories behind the theories。Still， v clear and easy enough for someone like me who isn't a computational neuroscientist。 This was a good， clear overview on how computer science/mathematical thinking has co-evolved with neurobiology。 I like it because it gives a meta-view on Thousand Brains and Predictive Processing instead of arguing for them directly。It was too long for me though。 I didn't find much value in the personal historical stories behind the theories。Still， v clear and easy enough for someone like me who isn't a computational neuroscientist。 。。。more
Mendoza
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Though it's elegantly written and impressive in its breadth， this book didn't quite completely work for me。 It attempts to do many things at once and ends up doing all of them passably。 In a nutshell， it is a history of the various ways in which mathematics are used in modeling different aspects of brain physiology and functionality。 If this sounds extremely general， it's because it is。To structure the discussion， the author associates each neuroscience （or psychology） problem with one mathemati Though it's elegantly written and impressive in its breadth， this book didn't quite completely work for me。 It attempts to do many things at once and ends up doing all of them passably。 In a nutshell， it is a history of the various ways in which mathematics are used in modeling different aspects of brain physiology and functionality。 If this sounds extremely general， it's because it is。To structure the discussion， the author associates each neuroscience （or psychology） problem with one mathematical （or physical） idea。 Thus spike production is described by differential equations。 Computing， memory forming are discussed using algebra。 Eyesight brings convolutions。 With movement we get some matrix theory。 Neural coding （of course） provides an opportunity to introduce information theory。 Mapping structure to function is paired with graph theory。 The discussion of rational decision making introduces Bayesian probabilities （Kudos here to the author for -correctly- attributing "Bayes' rule" to its actual author Laplace）。 A final chapter is dedicated to attempts at representing the entire brain using an analog of （Gibbs） free energy。It's a neat concept， but only leaves room for the most basic treatment of each topic， and so many interesting developments are just hinted at， or left out altogether。The price of this flitting about is that no single idea is discussed in any depth， and also there is no personal view given by the author of how all these approaches might add up to a science。 In the last chapter she appears to timidly hitch her wagon to Friston's "free energy" theories， but the problem here is that no-one seems to see a clear path to practical， useful results using this approach。 。。。more
Gustavo Juantorena
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Al momento de poner la calificación siempre me parece necesario compararlo con libros de temática similar y en este caso es claro que existe muy poco。 La Neurociencia Computacional es una sub-disciplina de la Neurociencia que no para de crecer y sin embargo no posee la misma popularidad que otras。 Me parece que el trabajo de divulgación de Grace Lindsay es muy bueno， logrando explicar con suficiente sencillez temas muchas veces áridos y poco abordados en la literatura de divulgación "neuro" más Al momento de poner la calificación siempre me parece necesario compararlo con libros de temática similar y en este caso es claro que existe muy poco。 La Neurociencia Computacional es una sub-disciplina de la Neurociencia que no para de crecer y sin embargo no posee la misma popularidad que otras。 Me parece que el trabajo de divulgación de Grace Lindsay es muy bueno， logrando explicar con suficiente sencillez temas muchas veces áridos y poco abordados en la literatura de divulgación "neuro" más clásica。 Recomendado para cualquier persona interesada en el funcionamiento del sistema nervioso y la inteligencia artificial。 。。。more
spencer
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great intro to a fast moving field。 accessible but uncompromising。 love the people-centric narrative。 especially loved the info theory and motor chapters
Rob GQ
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4。25
Brian Clegg
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This is a remarkable book。 When Ernest Rutherford made his infamous remark about science being either physics or stamp collecting， it was， of course， an exaggeration。 Yet it was based on a point - biology in particular was primarily about collecting information on what happened rather than explaining at a fundamental level why it happened。 This book shows how biologists， in collaboration with physicists， mathematicians and computer scientists， have moved on the science of the brain to model some This is a remarkable book。 When Ernest Rutherford made his infamous remark about science being either physics or stamp collecting， it was， of course， an exaggeration。 Yet it was based on a point - biology in particular was primarily about collecting information on what happened rather than explaining at a fundamental level why it happened。 This book shows how biologists， in collaboration with physicists， mathematicians and computer scientists， have moved on the science of the brain to model some of its underlying mechanisms。Grace Lindsay is careful to emphasise the very real difference between physical and biological problems。 Most systems studied by physics are a lot simpler than biological systems， making it easier to make effective mathematical and computational models。 But despite this， huge progress has been made drawing on tools and techniques developed for physics and computing to get a better picture of the mechanisms of the brain。In the book we see this from two directions - it's primarily about modelling the brain's processes and structures， but we also see how the field of artificial intelligence has learned a lot from what we know of the way the brain works （and doesn't work very well） in developing the latest generation of AI systems。 Lindsay shows how we have come to get a better understanding of the mechanisms of neutrons， memory formation， sight， decision making and more， looking at both the detailed level of neurons and larger scale structure。 Many of the chapters take us on entertaining diversions related to the history of the development of these ideas。 When I mentioned the book to someone who works in neurology， the response was that most computational neurology books they'd come across contained a barrage of equations - Lindsay does this with hardly an equation in the text （the only one I remember is Bayes theorem）， though there are a few in an appendix for those who like their content a bit crunchier。The only real criticism I have is that it could have done with some paring back。 The book felt a bit too long， too many people were name-checked， and too many bits of brain functionality were covered。 I also wouldn't have finished the book with a 'grand unified theories of the brain' chapter， which had too much of an overview feel and threw in concepts like consciousness that require whole books in their own right - it would have been better if the last chapter had pulled things together and looked forward to the next developments。 However， this remains an excellent introduction to an area that few of us probably know anything about， and all the more fascinating because of that。 。。。more
Asim
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If you are part of an underrepresented computational/theoretical group in a Neuroscience institute， forget giving talks to get people interested， simply buy a couple of copies of this book and hide them in random places for people to find。 I am pretty sure plenty of students will 'magically' start walking into your lab and asking questions！I believe this book fills a very important gap in mainstream （read: general public friendly） neuroscience books。 It nicely surveys the neuroscientific landsca If you are part of an underrepresented computational/theoretical group in a Neuroscience institute， forget giving talks to get people interested， simply buy a couple of copies of this book and hide them in random places for people to find。 I am pretty sure plenty of students will 'magically' start walking into your lab and asking questions！I believe this book fills a very important gap in mainstream （read: general public friendly） neuroscience books。 It nicely surveys the neuroscientific landscape from the perspective of quantitative approaches （Physics， Maths， Engineering） and personalities involved who shaped it。 The book also contains an appendix for the mentioned mathematics for the more inclined reader。 As an early career scientist who is dipping his feet into more theoretical approaches， I certainly found this volume both informative and inspirational。 Definitely would recommend！ 。。。more
Laurie
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