Societies of Brains: A Study in the Neuroscience of Love and Hate : The Spinoza Lectures : Amsterdam, Netherlands (The International Neural Networks)

Rating:
Summary: FuN with a capital N for Neuroscience!
Review: What a fun book. I was a polical science major, and expected to be in way over my head with matters relating to, well, real science. Luckily I was not. I picked it up after reading through the nifty stuff on the Freeman Lab site at UC Berkeley. I figured I ought to write up a little somethin' since the other reivew seems as if it's trying to scare people off.

That said, it was hard to tell who the intended audience of the book was. It wasn't for people with any sort of bio backgrounds, as he took the time to explain what hemoglobin was. But he also expected a pretty thorough understanding of epimistological models and how synapses function.

So I'm guessing cog-sci types. Neurology people who never took a philosophy class and are willing to think outside the box might get something out of it, as would philosophy kids who neglected their bio. I think most AI people would find some of it old hat, but a fun read none the less.

Traditional neural networks get pretty skewered. But skewering neural networks has become a lot more popular in the last five years since this book was published, I suppose, so hey. And Freeman does it a lot more eloquently than most.

Now, for the rest of us: As I said, I went into this book knowing relitively little. Which, in retrospect, was good. It was an epiphany. Last time I felt that way was reading Weber's "Prodestant Ethic..." when I was a freshman. As if the wool was being lifted from my eyes.

Knowing nothing about the field I can't really judge how revolutionary or archetypical the theories presented are. But if you know nothing about the field and just want some cerebral stimulation to get you excited about the topic, this is the place to be.

Rating:
Summary: FuN with a capital N for Neuroscience!
Review: What a fun book. I was a polical science major, and expected to be in way over my head with matters relating to, well, real science. Luckily I was not. I picked it up after reading through the nifty stuff on the Freeman Lab site at UC Berkeley. I figured I ought to write up a little somethin' since the other reivew seems as if it's trying to scare people off.

That said, it was hard to tell who the intended audience of the book was. It wasn't for people with any sort of bio backgrounds, as he took the time to explain what hemoglobin was. But he also expected a pretty thorough understanding of epimistological models and how synapses function.

So I'm guessing cog-sci types. Neurology people who never took a philosophy class and are willing to think outside the box might get something out of it, as would philosophy kids who neglected their bio. I think most AI people would find some of it old hat, but a fun read none the less.

Traditional neural networks get pretty skewered. But skewering neural networks has become a lot more popular in the last five years since this book was published, I suppose, so hey. And Freeman does it a lot more eloquently than most.

Now, for the rest of us: As I said, I went into this book knowing relitively little. Which, in retrospect, was good. It was an epiphany. Last time I felt that way was reading Weber's "Prodestant Ethic..." when I was a freshman. As if the wool was being lifted from my eyes.

Knowing nothing about the field I can't really judge how revolutionary or archetypical the theories presented are. But if you know nothing about the field and just want some cerebral stimulation to get you excited about the topic, this is the place to be.