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From Computer to Brain: Foundations of Computational Neuroscience

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Biology undergraduates, medical students and life-science graduate students often have limited mathematical skills. Similarly, physics, math and engineering students have little patience for the detailed facts that make up much of biological knowledge. Teaching computational neuroscience as an integrated discipline requires that both groups be brought forward onto common g Biology undergraduates, medical students and life-science graduate students often have limited mathematical skills. Similarly, physics, math and engineering students have little patience for the detailed facts that make up much of biological knowledge. Teaching computational neuroscience as an integrated discipline requires that both groups be brought forward onto common ground. This book does this by making ancillary material available in an appendix and providing basic explanations without becoming bogged down in unnecessary details. The book will be suitable for undergraduates and beginning graduate students taking a computational neuroscience course and also to anyone with an interest in the uses of the computer in modeling the nervous system.


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Biology undergraduates, medical students and life-science graduate students often have limited mathematical skills. Similarly, physics, math and engineering students have little patience for the detailed facts that make up much of biological knowledge. Teaching computational neuroscience as an integrated discipline requires that both groups be brought forward onto common g Biology undergraduates, medical students and life-science graduate students often have limited mathematical skills. Similarly, physics, math and engineering students have little patience for the detailed facts that make up much of biological knowledge. Teaching computational neuroscience as an integrated discipline requires that both groups be brought forward onto common ground. This book does this by making ancillary material available in an appendix and providing basic explanations without becoming bogged down in unnecessary details. The book will be suitable for undergraduates and beginning graduate students taking a computational neuroscience course and also to anyone with an interest in the uses of the computer in modeling the nervous system.

37 review for From Computer to Brain: Foundations of Computational Neuroscience

  1. 5 out of 5

    Per-Yngve Ingensson

    Overall this book was both surprisingly easy to read and immensely informative, not to mention humorous. The author comes across as someone you'd want to meet. However, there breakdowns, especially towards the end, for instance in describing analog electric models of neurons. I'll admit that the author needs to make trade-offs in the level of exposition, and that I like and have an easier time with discrete/digital modeling than analog things, but omitting calculus from the description probably o Overall this book was both surprisingly easy to read and immensely informative, not to mention humorous. The author comes across as someone you'd want to meet. However, there breakdowns, especially towards the end, for instance in describing analog electric models of neurons. I'll admit that the author needs to make trade-offs in the level of exposition, and that I like and have an easier time with discrete/digital modeling than analog things, but omitting calculus from the description probably only muddied the waters. The author uses an unfamiliar numerical approximation of the differential equations involved and then doesn't explain them enough, in my opinion. So I ended up skipping that section, and then the Hodgkin-Huxley model too, because I assume it builds on the same material. I ended up heading to the final two chapters and even then the way he described neural circuits seemed sort of hazy and rushed. The same could have been said of some of the other passages: for instance, some of the explanations about the factors involved in limulus modeling were perplexing. But don't get the impression that I'm just finding fault. I'm writing the review like this because saying all the good things about this book would take longer than saying all the bad things about it. A lot of the exposition is both clear and enthralling. It is shown that without compensation, lateral inhibition in the retina might lead to a bright halo around the whole field of vision; that the digestive tract will function in lieu of a brain, as if we have a simple worm living inside all of us; that a number of the brain's messaging systems are derived from our own cellular shit: "Are you thinking about your food, or is your food thinking about you?" On top of that I finally have a (somewhat) better understanding of the Hopfield neural network model and how it may have a kernel of biological plausibility. It just seems like the author, though brilliant, was pressured by a deadline. If a second edition comes out I'll look forward to it. Last word: I really do like the Computer Modern Roman typeface used in this book. It's an acquired taste; not anywhere near as refined as, say, Palatino; and is usually associated with much more painful reading but I have fond memories of it nonetheless.

  2. 4 out of 5

    Ryan

    Super great intro to computational neuroscience, though aimed at undergrads so there is little depth!

  3. 4 out of 5

    Alex Vogel

  4. 4 out of 5

    Ramanuja

  5. 5 out of 5

    Kaiser

  6. 5 out of 5

    Alex Telfar

  7. 4 out of 5

    Jovany Agathe

  8. 5 out of 5

    Peter Jedlicka

  9. 5 out of 5

    Giulia✨

  10. 5 out of 5

    Martin

  11. 4 out of 5

    Jan Kreps

  12. 5 out of 5

    Pedro

  13. 5 out of 5

    Mount

  14. 5 out of 5

    Will Wei

    A total but simple and interesting guide to readers who want to entry this area.

  15. 4 out of 5

    Brian Holt

  16. 4 out of 5

    Keira

  17. 4 out of 5

    Seyed Yahya Moradi

  18. 4 out of 5

    S.javad Mousavi

  19. 4 out of 5

    Ouroboros

  20. 4 out of 5

    Marco

  21. 4 out of 5

    Gregory Venezia

  22. 5 out of 5

    Hamidreza Ghodsi

  23. 4 out of 5

    Razan Ghuraibi

  24. 4 out of 5

    Chris Clayton

  25. 4 out of 5

    Leila

  26. 4 out of 5

    Rafael Suleiman

  27. 4 out of 5

    James Tauber

  28. 5 out of 5

    Osama Anwar

  29. 4 out of 5

    Nitin Rughoonauth

  30. 4 out of 5

    نجلاء العريفي

  31. 5 out of 5

    Thiz

  32. 4 out of 5

    Ludwig

  33. 4 out of 5

    Aubrey Moat

  34. 5 out of 5

    glolry hapiness Miracle

  35. 4 out of 5

    Drifting-Ronin

  36. 4 out of 5

    Yasemin Ünal

  37. 4 out of 5

    Miguel Veliz

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