Skip to main content

Models of the Mind - Grace Lindsay *****

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.



Using these links earns us commission at no cost to you
Review by Brian Clegg


Popular posts from this blog

The Ten Equations that Rule the World - David Sumpter ****

David Sumpter makes it clear in this book that a couple of handfuls of equations have a huge influence on our everyday lives. I needed an equation too to give this book a star rating - I’ve never had one where there was such a divergence of feeling about it. I wanted to give it five stars for the exposition of the power and importance of these equations and just two stars for an aspect of the way that Sumpter did it. The fact that the outcome of applying my star balancing equation was four stars emphasises how good the content is. What we have here is ten key equations from applied mathematics. (Strictly, nine, as the tenth isn’t really an equation, it’s the programmer’s favourite ‘If… then…’ - though as a programmer I was always more an ‘If… then… else…’ fan.) Those equations range from the magnificent one behind Bayesian statistics and the predictive power of logistic regression to the method of determining confidence intervals and the kind of influencer matrix so beloved of social m

How to Read Numbers - Tom Chivers and David Chivers *****

This is one of my favourite kinds of book - it takes on the way statistics are presented to us, points out flaws and pitfalls, and gives clear guidance on how to do it better. The Chivers brothers' book isn't particularly new in doing this - for example, Michael Blastland and Andrew Dilnot did something similar in the excellent 2007 title The Tiger that Isn't - but it's good to have an up-to-date take on the subject, and How to Read Numbers gives us both some excellent new examples and highlights errors that are more common now. The relatively slim title (and that's a good thing) takes the reader through a whole host of things that can go wrong. So, for example, they explore the dangers of anecdotal evidence, tell of study samples that are too small or badly selected, explore the easily misunderstood meaning of 'statistical significance', consider confounders, effect size, absolute versus relative risk, rankings, cherry picking and more. This is all done i