Models of the Mind

How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain

By: Grace Lindsay

Narrated by: Wendy Tremont King

Length: 13 hrs

Publisher's summary

Grace Lindsay reveals the value of describing the machinery of neuroscience using the elegant language of mathematics.

The brain is made up of 85 billion neurons, which are connected by over 100 trillion synapses. For over a century, a diverse array of researchers have been trying to find a language that can be used to capture the essence of what these neurons do and how they communicate - and how those communications create thoughts, perceptions, and actions. The language they were looking for was mathematics, and we would not be able to understand the brain as we do today without it.

In Models of the Mind, author and computational neuroscientist Grace Lindsay explains how mathematical models have allowed scientists to understand and describe many of the brain's processes, including decision-making, sensory processing, quantifying memory, and more. She introduces listeners to the most important concepts in modern neuroscience, and highlights the tensions that arise when bringing the abstract world of mathematical modelling into contact with the messy details of biology.

©2021 Grace Lindsay (P)2021 Tantor

Elegant review of neuroscience

Loved every second of the book!
Interesting and thought provoking book narrated brilliantly .
The only reason to put only 4 stars on the overall performance is the missing pdf file with the accompanying graphics.

ENGAGING

Good 👍 stuff and wide ranging perspectives luckily presented and reviews in a comfortable mannety

Nice meta-analysis of neuroscience & AI

I've been reading about the convergence of neuroscience and artificial intelligence (AI) for over two decades. Models of the Mind provides a helpful meta-analysis of models of the mind shaped by philosophy, physics, biology, Mathematics, engineering, among other things. it's very approachable for the average lay person.

The author addresses studies inspired by observations of individual nerve cells, as well as analysis of electrical signal outputs from areas that fire in unison like a chorus of screaming demons. She ties it all together by referring to the history of information theory.

Readers can ponder the implications of a clear explanation of Baysean models of how prior knowledge weighs in to decision making calculations. The conversation about the tug and pull of grand unifying theories and practical applications is thought provoking. I enjoyed it.

A must for those who want an introduction to the intersection of neuroscience and AI

This book does a great job providing a historical overview of the field of computational neuroscience.

Unique take on neuroscience

I’ve been interested in neuroscience for a while, but sometimes, books on the topic aren’t written for the average person. I’ve heard a lot of talk about this book from Grace Lindsay, so I decided to check it out even though I’m not much of a math guy, which scared me even more. Fortunately, Grace did an incredible job of making this book accessible to the average reader like myself. Throughout the book, the author not only breaks down complex topics, but you also learn the history of neuroscience as well as how mathematics and models have helped us understand the brain. There were still a few parts that I didn’t quite grasp, but for the most part, I was able to keep up, so I think many people would enjoy this book if they’re interested in the brain. Some of my favorite chapters were in the realm of topics I’m more familiar with such as reward-based learning, decision making, and some others. So, if you like this topic at all or are curious to get into it, I’m sure you’ll enjoy this book.

Great coverage

Covers so many interesting models. Each chapter discusses a model or a family of models related to different aspects of the brain and the mind in increasing levels: starting from action potential level models, neuron modeling, to simple and than complex neural networks to models of learning and unified theories of the mind. Only downside: I don't know why but I couldn't find an accompanying pdf which certainly would add when more content and clarity to the book with figures and equations.

content is great. The reader is slow and pedantic

Just speed up the reader to 1.25 and you'll be fine. Content wise, a very well described history and present state of the art of computational neuroscience. I will try to incorporate it into a course.