Chapter 10: What's in a Species?

• Introduction
• It is unusual to have only one species from an evolutionary group
• Times when there are multiple hominid species: 50 to 100 thousand years.
• Total time that hominids have been around: 1 million years.
• Definitions
• The homo genus is controversial
• Some say that Neanderthals, etc. should be in that genus.
• Some say that Boskops were a separate species.
• Fallacies of the notion of race
• Separate gene pools vs. interbreeding populations.
• Not strict categories
• Eugenics programs
• Have occurred throughout history
• Part of the problem is how do you define "better"
• Surprising list of people who have supported such movements
• Winston Churchill - sterilizing the "feeble minded"
• Alexander Graham Bell - sterilizing the congenitally deaf
• George Bernard Shaw - involved in the eugenics movement
• Races Versus Gene Pools
• Genetic variation in a subgroup is just as common as in the entire population
• May be restricted in what can vary.
• The absolute amount of variation is the same.
  

Notes on Chapter 9: From Brain Differences to Individual Differences

The mail takeaway I got from this chapter was that genes are an important aspect of a person's potential, but they only determine about half of the traits a person might have.  The rest are determined by the "environment."  "Environment" in this case can mean things as diverse as diet, training, interaction with microbes, etc.


The authors seemed to be careful to point out the limits of genetics and the problems with trying to define just what a "superior" person is.  Three examples are given of people who can be seen as handicapped in some way, and yet possessing significant advantages in another way.

The authors describe the results of studies using fMRIs and reading skills which indicate a correlation between reading and certain brain configurations.  The problem is that having two related traits does not tell you whether one trait causes the other or if they are both related to some other trait. 
For example: do people with good reading skills cause their brain to be configured a different way, or does having a particular configuration mean that the person is a good reader?  Another possibility could be that people with good reading skills receive abundant dietary essential fatty acids during a key period of growth.

Diffusion Tensor Imaging

According to an article I found, Diffusion Tensor Imaging (DTI) reveals the circuits in the brain that are activated during a given activity.   DTI is another form Magnetic Resonance Imaging (MRI) that tracks the large movements of water molecules.  Since blood is largely water, this sort of scan should show the routes that blood is flowing to and hence show the neural pathways that are being activated.

Contrast this with Functional MRI, which shows where carbohydrates (sugar) is being consumed in the brain.

Chapter 9: From Brain Differences to Individual Differences

• Introduction
• Do genetics predispose individuals to have different abilities
• Examples of people with different brains/abilities
• Les
• Premature, blind, only expected to live a short time
• Incredible memory, natural concert pianist
• Willa
• Williams Syndrome
• Brain is 15% smaller than average
• At 18, roughly the level of a first grader
• Gifted writer
• Kim
• Missing corpu collasum and anterior commissure
• Can perfectly recall anything he has read
• Similar to the "rain man" in terms of difficulties with day to day life.
• Very minor genetic changes resulted in these differences
• What sorts of changes could bring about these changes?
• Brain Paths
• Diffusion Tensor Imaging (DTI) 
• MRI that traces paths through the brain
• May lead to predispositions due to genetic factors
• Genetic control is highly indirect.
• Example with the rat rewiring its brain to repair damage.
• Brain Tracts and Differential Abilities
• Reading ability could be tied to brain wiring
• Nature vs. Nurture
• Genetics control roughly 1/2 of our brain traits.
• Someone who is predisposed to having a trait my still not develop that trait.
• Based on identical twin, fraternal twin and adopted sibling studies.

Notes on Chapter 8: The Tools of Thought

I found this chapter to one of the longer and more difficult to comprehend chapters in the book.  The basic ideas were intriguing, in particular:

• Memories are hierarchical.
• Recognition is a multi-stage, temporal process.
• Brains are naturally amenable to grammar like organization.

A basic concept used throughout the book is that larger brains lead to new abilities, even though the larger brain is just "more of the same" in that a larger brain does not have new or different structures, just larger versions of the same thing.

Thus, if memories are hierarchical, then a larger brain means more categories and deeper hierarchies.  If recognition is multi-stage, then a bigger brain can mean more stages and a finer distinction between otherwise identical items.  If brains and thought uses a grammar like structure, then a larger brain could mean a more elaborate grammar.

I find the notion of the "scavenger hunt" model for memory especially interesting.  While that approach seems especially strange, it does dovetail nicely with the associative nature of the brain: each "step" in a memory is an association.  Each association can be reused in different memories because they have a certain "momentum:" a memory is the sum of what came before plus the next association.

Chapter 8: The Tools of Thought

• Feedback and hierarchies of cortical circuits
• If our brains is the same as a mammal brain, just larger, how does it gain new abilities?
• Feedback Circuits
• The cortex is wired both from sensory systems to something else and from the cortex to the sensory systems.
• Can re-experience sensations without the actual stimulation.
• Can rearrange and examine the steps that are taken.
• Steps of Recognition
1. Initial Activation
• The flower example
• Sensory impression
• Contains more data than the brain can process
2. Learning
• Group different flowers into categories
• Group all flowers into plants
• Generalizing and differentiating
• Generalizing: flowers, plants, commonalities
• Differentiating: what makes a rose different from a daisy
3. Feedback
• Once a category is established, feedback suppresses differences
• Thalamus is involved in suppression
• Effect is that the cortex only sees the common aspects in the first sensation.
• Next sessions, the differences start to arise.
• Thus an instantaneous experience becomes a sequence of experiences.
• Sequences
• In addition to breaking a single sensation into multiple steps as per categories.
• Flower petal example: start with one petal, look for the next.
• Validate a possibility
• What One Brain Area Tells Another Brain Area
• Old tools are relatively crude
• EEG, PET scan, CAT scan
• Includes fMRI
• What's in an Image?
• Description of how fMRI works.
• Putting it Together: From Generalists to Specialists
• Fallacy: the wheels moving the car
• Something else actually turns the wheels
• Controversy over recognition
• Grandmother cells
• Specific cells encode information.
• Distributed
• populations of cooperating neurons represent information
• Memory Construction
• They advocate a hybrid approach between Grandmother cells an a distributed system.
• Distributed in the sense that no one neuron encodes a particular memory
• "Grandmother" like in that neurons do exist that are very highly tuned to a particular type of image
• A sort of "shared grandmother cell" approach.
• Building High-Level Cognition
• Large brains lead to long cortical processing areas.
• Libraries and Labyrinths
• Memories are stored as routes through the brain instead of one location that has all the data.
• Scavenger hunt analogy
• Grammars of the Brain
• The way the brain works is similar to a linguistic grammar
• There are rules that govern what sequences of thoughts are allowable
• Specific pathways store individual memories.
  

Notes on Chapter 7: The Thinking Brain

I found this to be a rather discordant chapter.  It starts by talking about evolution of the brain in terms of size and then switches over to a discussion of how the motor systems work. I think the point that the authors are trying to make is that the association regions got larger so that it would be possible to make more elaborate and detailed plans. 

Another point is that the brain deals with detail hierarchically.  With the pitching example, the planning portion of the brain deals with the high level concepts - use a slider pitch instead of a fastball - and then passes that command onto the next brain system.  That brain system selects the general set of movements required and passes each off to the motor portions.  The motor portions control the muscle movements for each step and cause the actual neural signals to be sent out.

The last part of the chapter portrays a struggle between the higher and lower portions of the brain for control.  The discussion is vaguely related to what came before (higher levels controlling lower levels) but it is portrayed more as a struggle than a cooperating system.  The authors note that the larger the brain is, generally the more control the higher portions of the brain have over the lower portions.

To illustrate the conflict aspect, the authors mention a movie where the conscious and subconscious minds are in what amounts to a pitched battle over control.

Chapter 7: The Thinking Brain

• Introduction
• How did we get big brains?
• Irish Elk analogy 
• Our brain is a scaled up version of a monkey brain
• Question becomes why are we so large?
• Mammal brains scale consistently
• Primate brains scale very predictably
• Extending thinking over time
• Various parts operate in concert
• The pitcher example
• Cortex: select a pitch
• Cortex tells the motor context 
• Motor cortex selects the individual, high level movements
• Motor cortex tells the striatum to perform a lower level movement
• Striatum initiates the individual movements with the body
• This is the basis for higher level planning
• The Cortex Takes Charge
• Larger cortex means more control over the lower portions of the brain and body.

Notes on Chapter 6: From Olfaction to Cognition

I found this to be a challenging chapter because it introduced a large amount of technical material that I was unfamiliar with.  In addition, I found it difficult to follow the discussion of just how one gets from olfaction to cognition - the supposed point of the chapter.

The section "From Cortext to Behavior" had the best discussion of this concept but was by no means clear.  Here is what I came away with:

• Different combinations of brain structures lead to different information about a sensation.
• Thalamo-cortical loop: what the smell means
• Cortex/amygdala: emotions or states that result from the smell, for example, hunger
• Cortex/Hippocampus: ability to recognize the context of the situation.
• Cortex: ability to form a plan such as how to approach the food.

One question that I have is what did other people come away with?  Was it basically similar to this or did they get something else out of it?

The next claim the book makes is that random access/associative regions emerged first, then point to point systems emerged.  The traditional view is the reverse.  How has this theory fared in the years since the book was published?

Chapter 6: From Olfaction to Cognition

• Introduction
• The major brain structures
• Striatum 
• involved with movement
• Stimulating the striatum can cause jerky, twitching movements.
• Prof. Delgado and the bulls
• Amygdala
• Controls the hypothalamus
• Hypothalamus links the neural system to the endocrine system.
• Involved in primitive emotion
• "Terminal Man" anecdote
• Hippocampus
• Memory
• Henry anecdote
• Receiving desk anecdote for memory
  
• Thalamo-cortical loops
• Frontal cortex and the thalmus
• Dorso-medial nucleus (DMN)
• Planning and outcomes
• All the systems act in concert
• From Cortext to behavior
• One sensation can trigger multiple thalamo-coritcal messages
• example: smell something
• thalamo-cortical loop
• what is it that you smelled (food)
• what could happen as a result of smelling (eating)
• olfactory cortex/amygdala - hunger
• cortex/hippocampus
• recognize the setting
• combine with stimuli and past outcomes
• Arrive at a predicted outcome to a course of action
• Neocortex
• Early mammals depended on smell and hearing
• Visual systems lagged behind
• Most brain expansion was dominated by random access/associative structures rather than point to point.
• Traditional view is the reverse: expansion was in point to point systems and then the associative areas formed later.
  

Notes on Chapter 5: Brains of Mammals

This chapter was fairly short but it was also pretty focused on describing how the various mechanisms of the brain work. 

Associative Cortical Circuits
A central concept to the book is the idea that there are two broad categories of sensory processing regions in the brain: point to point and "random access" (I refer to this as associative).  The idea is that, for systems like the motor regions, if one muscle is physically situated near another muscle, then the corresponding brain regions will also be located near each other.

This is not the case for the brain region that processes the olfactory senses.  Instead that region follows what the authors call a "random access" scheme.  In that arrangement, while the primary sensory inputs are point to point, they quickly lose that aspect.  For example, the scent of oranges might be associated with food, or the scent of a mountain lion could be associated with the notion of danger.

I call this arrangement "associative" because it hearkens to the notion that a primary sensation (in this case a smell) is associated with something else. 

Why would being warm blooded help "absorb the cost" for having a large brain?
The authors mention several times that having a large brain is biologically expensive.  One issue is the number of calories consumed by neuronal tissues.  I suppose one reason that being warm blooded might help is that, if the organism is going to be consuming a lot of calories to keep the body at a constant temperature, then the cost of having a large brain would be, proportionally speaking, smaller.

Nevertheless, it seems to me that an organism that needs additional calories to support itself because of a large brain would still be at a disadvantage relative to one that had a smaller brain.

Chapter 5: Brains of Mammals

• Introduction
• Olfactory brain is sheet-like, similar in structure to the cortex.
• Neurons and Networks
• Discussion of how a signal travels down a neuron.
• Olfactory is associative not point to point like vision or touch.
• Description of the olfactory system.
• Learning
• Description of neuron learning
• Once the olfactory system became able to learn, it could be used for anything, not just smell.

Notes on Chapter 4: Brains Arrive

This chapter has a number of points that are central to the book:

• The olfactory system is associative instead of being point to point.
• When brains get larger, they do so disproportionately.
• Big brains are expensive.

The Olfactory Brain Regions are Associative
The important thing about associated vs. point to point is that the authors argue that the foundation for the neocortex  already existed long before primates with their larger brains showed up.  One of the primary abilities of the cortex is its ability to associate anything with anything else - something the olfactory system must be able to do in order to associate a particular smell with some attribute.

This makes for a more "natural" transition from the smaller brains to larger human brains.  Nothing special is required to explain how the cortex becomes associative: it evolved from the olfactory regions.

When Brains Become Larger, they do so Disproportionately
Disproportionate growth in brain areas means that, rather than the motor and sensory parts of the brain increasing at the same rate as the cortex, more expansion can go to the higher brain functions instead of the lower ones.  According to the authors, this trend is already apparent with birds and mammals, whose growth patterns are different from those in fish and reptiles.

Another point about brain evolution is that the proportional sizes of different brain regions are more or less constant for different classes of animals.  That is, the brain regions of a larger animal such as a moose, are proportionally the same size as those of a smaller animal such as a mouse.  Because the moose is bigger, it has a larger brain, but the relative sizes are more or less the same.

Large Brains are Expensive
The authors point out additional costs of big brains later on in the book.  For now, the point about the metabolic cost is highlighted.  In looking at the way that humans will keep dairy animals and noting that this gives them access to an extra 100 calories or so, it is clear that having a larger brain that confers no corresponding advantage is serious drawback.

And just to make the cost more onerous, 50% of calories for nervous tissue must come from carbohydrates.  Normally, calories come from starches and sugars, so this is not an issue, but under starvation conditions, these calories come at the expense of cannibalizing lean body tissues.

Chapter 4: Brains Arrive

• Introduction
• In early animals, the nervous system is mostly about processing senses and controlling movement.
• As brains increase in size, disproportionate amount of the new space go to internal processing.
• First Brains
• Point to point, sensory systems.
• The striatum is an organizer for signals
• Motor outputs
• The different motor regions are very different from each other.
• The olfactory part of the brain is different from the other sensory systems
• Associative instead of point to point.
• Brain Expansion
• The size of the brain relative to the size of the animal is constant in fish and reptiles.
• But not for mammals or birds
• Protomammals develop better hearing and olfaction
• Birds develop better vision
• Brain size in both jumps 300%
• Note that a larger brain can be a disadvantage
• Higher metabolic cost
• The protomammals were evolving "wildly" because of intense competition with the dinosaurs.

Notes on Chapter 3: Genes Build Brains

Compressed Genomes

The description of how genes work, in particular that they do not code to all possible variations, only ones that fit a particular pattern, is appealing to me because it makes intuitive sense.  What is the point of coding to variations that are not viable?

Reasoning like this is dangerously close to asserting a "design" in the human genome instead of assuming that variation is random, something that I think of as a pitfall.  That being said, it does make a lot of sense and seems advantageous for an animal because it would take less energy to replicate the genome.  Furthermore, mutations are less liable to be catastrophic because most of the variation occurs in areas that do not have that effect on an organism.

Non-Genetic Variation

The idea that there is a limit to how much control genes have on brain makeup is also attractive because it seems to fit the evidence.  There are cases where a person could get Alzheimer's and their identical twin does not.  This sort of thing is hard to explain if all aspects of the brain are genetically controlled.

Furthermore, there is precedent for non-genetic control of different aspects of the organism.  For example, identical twins usually have different fingerprints because the patterns on fingers vary due to blood flow in the womb instead of being completely determined by genes.

Chapter 3: Genes Build Brains

• Introduction
• Evolution is random, not directed.
• Humans are what has survived, not the pinnacle.
• Other results were possible, we just happened to survive.
• How Much Variation can Occur?
• Basic Genetics
• Basic unit of a strand of DNA is a base pair.
• A group of 3 adjacent base pairs form a codon.
• Each codon maps to one of 20 amino acids.
• A string of codons (with a start and stop codon) make up a gene.
• A gene codes to a protein.
• Most of the DNA occurs outside genes (introns)
• There are around 25,000 genes in the human genome.
• Odd factoid
• Human and chimpanzee genomes differ by only about 3%.
• But one human being's genome may differ from another's by up to 12%.
• Which genes are varying is more important than the number of genes.
• Blueprint Systems
• Compares computer code to genes.
• Computer code is very brittle, i.e. changing a bit in a program could seriously impair the program
• Genes are more resilient
• Bundling Genes
• Variation is random, but it is constrained
• Genes are a sort of constrained language
• Makes them more resilient but also limits them
• Genes only code to viable combinations, not all combinations.
• Large Brains are Expensive
• Nervous tissue requires twice as many calories as others
• Note that 50% of that must come from carbohydrates
• Requires large animal at birth
• Increases risk to the mother
• If it is expensive, why do humans have them?
• Common theory is that intelligence is strongly selected for.
• Theory in this book
• Larger brain was an "accident"
• Larger brain had additional utility
• Then the species adapted to having larger and larger brains.

Notes on Chapter 2: Mind in the Machine

This chapter introduces the key concept of point to point vs. associative brain systems.  Associative systems are important to the rest of the book because they form the basis of what the authors claim is the foundation to intelligence - the ability to associate two otherwise unrelated pieces of information.

The sensory system that serves as the primary example of this--smell--is also key because the authors will argue that this sense was very important to early mammals.

The part of the chapter that compares and contrasts computers with human brains highlights the ways that brains and computers differ.

• A brain always comes with an associated body.  A computer has a case, peripherals, etc. but it cannot really do anything on its own.  
• A brain will learn from experience and modify its behaviors.  Generally speaking, a computer will not learn from experience nor will it spontaneously change its behavior.
• Changing the size of the brain results in new and different abilities.  Making a computer faster or giving it more memory generally does not change its capabilities.

I don't entirely buy into the argument that increasing computer power does not result in new capabilities.  Improvements to computer speed, size, user interface and storage have transformed the way that they are used.  They have gone from being a very specialized piece of equipment to an almost indispensable tool that everyone uses.

Computers from 1940 to 1970 were extremely difficult to use and were applied to very specialized applications.  From 1970 to 1990 computers began to be accessible to the average person, being incorporated in everything from toasters to traffic signals.  The user interface also changes from things like paper tapes and punch cards tapes to screens and floppy drives.  From 1990 to 2010, computers have become increasingly useful until now a business person wouldn't look dressed without a laptop.