Tahsin’s Reading List: Neuroscience & NeuroEngineering

 

#BookRecommendation

 

Cognitive Neuroscience

 

Computational Neuroscience

Neuroscience

Consciousness & Neuroscience

Neuroscience & Spirituality

 

Psychology

Cognitive Science

Behavioral Economics

Books Authored by Computer Scientists, Cognitive Scientists

 

 

Books Authored by Physicists

Books Authored by Neurologists

  • Books by Dr V. S. Ramachandran [Area of Research: Neurology; Neuroscience]
    • Curious Neurological cases and interesting explanations by Dr Ramachandran.

 

 

 

  • Books by Dr Antonio Damasio [Area of Research: Neurology; Neuroscience]
    • Dr Damasio’s books focus on Emotion, Feelings and Consciousness.

 

Books Authored by Psychiatrists

 

Books Authored by Neuroscientists

 

 

 

 

Books Authored by Psychologists, Linguists, Cognitive Scientists

 

Books Authored by Philosophers

 

Science & Engineering News: Neuroengineering Aims to Empower the Paralyzed and More ..

 

Neuroengineering

#MedicineAndBioEngineering  #NeuroEngineering

“… by maneuvering his right shoulder in certain ways, Mumford could send signals through the stimulator and down his left arm into the muscles of his hand … He [Previously paralyzed] could open the refrigerator, take out a sandwich, and eat it on his own.

Advances in brain-machine interfaces and electrical–stimulation devices are generating marvelous research results in people with paralysis—some are using their thoughts to control robotic arms, and others are taking tentative steps.

250,000 people with spinal-cord injuries in the United States alone.

Retinal implant that can restore sight to people with a hereditary form of blindness. Potential market is quite big—perhaps 1.5 million people worldwide and 100,000 in the United States

Tahsin’s Recommendations: Books on Neurology, Neuroscience, Psychology & Cognitive Science

 

Tahsin’s Recommendations:

Books on Neurology: Books by Neurologists

Books on Neuroscience

Books on Psychological and Cognitive Sciences

#BookRecommendation

 

Cognitive Neuroscience

 

Books Authored by Neurologists, Neuroscientists, Psychologists & Computer Scientists

 

 

  • Books by Dr V. S. Ramachandran [Areas of Research: Neurology; Neuroscience] 
    • Curious Neurological cases and interesting explanations.

 

  • Books by Dr Oliver Sacks [Areas of Research: Neurology; Neuroscience]
    • Neurological Case Studies. Each chapter in his books features a curious neurological patient with a curious disorder. 

 

 

  • Books by Dr Antonio Damasio [Areas of Research: Neurology; Neuroscience]
    • Dr Damasio’s books focus on the topics of Emotion, Feelings and Consciousness.

 

 

Science & Engineering, Medicine & Innovation: “Jurassic” Theme Park, Physical Digital Retail & More

 

Science & Engineering, Medicine & Innovation: “Jurassic” Theme Park, Physical Digital Retail & More

Bio-Electronic Medicine 

#BioEngineering #Medicine #MolecularMedicine

“Stimulation of the nervous system could replace drugs for inflammatory and autoimmune conditions”

 

Paleontology – Molecular Biology 

“Jurassic” Theme Park

#ThemeParkDevelopment #BioEngineering #MolecularBio #Biotechnology  #Paleontology

 

Has Science advanced to the point when it can enable us create “Jurassic” Theme Parks featuring Dinosaurs?

“A father-son duo of biologists has set the stage for so-called de-extinction.”

 

 

Computational Neuroscience & Neuro-Engineering  

#ComputationalNeuroscience #NeuroEngineering #Neuroscience #CognitiveScience

 

Google  #PhysicalDigitalRetail

 

Physical Digital Retail  #PhysicalDigitalRetail

 

Inspiration List For My “To-be-written” Books

Inspiration List For My To-be-written Books

[11.22.14]

#BookRecommendation

 

Physical Computing

  1. The Society of Mind [Paperback] by Marvin Minsky (Amazon)
  2. The Emotion Machine – Marvin Minsky
  3. How to create a Mind – Ray Kurzweil
  4. On Intelligence – Jeff Hawkins
  5. How the Mind Works – Steven Pinker


Education; Neuroscience; Psychology; Cognitive Science


Neuroscience
  1. Phantoms in the Brain – V S Ramachandran
  2. The Tell Tale Brain – V S Ramachandran
  3. The man Who Mistook his Wife for a Hat – Oliver Sacks
  4. Musicophilia – Oliver Sacks
  5. Hallucinations – Oliver Sacks
  6. Consciousness – Antonio Damasio
  7. The Second Nature – Gerald Edelman
  8. How the Mind Works – Steven Pinker


Linguistics; Cognitive Science
  1. The Language Instinct – Steven Pinker


Expertise; Complexity; Human Organization; Managing Complexity
  1. The Checklist Manifesto – Atul Gawande, MD


Future of Technology
  1. Abundance – Peter Diamandis, MD
  2. The Singularity is near – Ray Kurzweil
  3. Physics of the Future – Michio Kaku, PhD
  4. The Future – Al Gore
  5. The Road Ahead – Bill Gates [1995; “Information Superhighway” which came to be known as the “Internet”, and the “Web” on top of it]
  6. The New Digital Age: Transforming Nations, Businesses, and Our Lives by Eric Schmidt, Jared Cohen




Technology in Business:
  1. Business @ The Speed of Thought – Bill Gates


Management and Innovation at Tech Companies

  1. How Google Works by Eric Schmidt & Jonathan Rosenberg 
 


Brain Sciences

 
Economics; Behavioral Economics
  1. Thinking Fast and Slow – Daniel Kahneman


Creativity; Process of Inventions & Discoveries
  1. Imagine – Jonah Lehrer
  2. Where Good Ideas Come From by Steven Johnson


Memory; Expertise
  1. Moonwalking with Einstein – Joshua Foer


Marketing; Business; Psychology
  1. Influence – Robert Cialdini, PhD


Human Decision Making
  1. How we decide – Jonah Lehrer
  2. Blink – Malcolm Gladwell
  3. Thinking Fast and Slow – Daniel Kahnemann; PhD [Behavioral Economics; Behavioral Finance]


MetaCognition; Critical Thinking; Creativity
  1. How to Solve It: A New Aspect of Mathematical Method by George Polya
  2. The Thinker’s Way – John Chaffee, PhD [Critical Thinking; Philosopher’s Tools]
  3. Think Like A Champ – Donald Trump [Success in Business and Life]
  4. Think Like A Freak – Steven Levitt, Stephen Dubner
  5. The Organized Mind: Thinking Straight in the Age of Information Overload by Daniel J Levitin (Amazon)

The way our Brains work & Its relation to “Stereotyping”

The way our Brains work & Its relation to “Stereotyping”

#CognitiveScience #NeuroScience #Microsoft  #MarketingStrategy #ArtificialIntelligence

 

The way human brain works is by building “model”s of how the world works. 

[See: Short Review of “How to create a Mind” by Ray Kurzweil and “On Intelligence” by Jeff Hawkins [TahsinVersion2.com] ]

Each concept that we learn, we build an internal model of that conceptThere is a “summarized model” of each concept we  learn – that comes to our mind just as we think of that concept.  

So, for example, thinking of “Microsoft” could remind you of Bill Gates or the Windows Operating System you have on your laptop. But Microsoft is not just Bill Gates or not just Windows. Gates or Windows are only the “summarized model or representation” of Microsoft in your brain.
 
The problem with this is that it could make us fall into the trap of “stereotyping” the world and not reflect the totality of a concept but only a part of it.

As an instance, it might happen that you have read the famous novel “Godfather” by Mario Puzo and from that point on, whenever you hear of Italy or Italians, you are reminded of Italian Mafia. But that’s stereotyping. Not all Italians are part of a Mafia gang.
 
How do we build these models?
 
We build these models as we learn concepts, possibly in a social context. 
This applies to every domain.

Let me give you an example from Marketing.
 
A few days back I wrote:

Microsoft has lost it’s “Brand Appeal” in the past few years that it once enjoyed. Google and Apple lead Microsoft in terms of “Brand Appeal”. 


When you think of Google or Apple products you think of them as being “cool”, “awesome”, “wonderful”, and so on.
 
How?
 
That’s how you learned about Google or Apple. You heard your friends say, “Apple products are so cool” and that’s how the model of “Apple products” in your mind got represented, as being something “cool”
 
In Marketing jargon, it’s called “word of mouth”advertising through the mouth of satisfied customers.
 
“Brand Appeal” depends more on what people “think” of products than the products themselves.
 
It might be the case that Microsoft products are better, but people are not doing enough of those “Wow”s – 

“Windows is so cool!”  

or 

Surface is simply sensational!”

In other words, “Brand Appeal” could fall victim to human stereotyping.
The effect is not just on customers and consumers, but also on job seekers – when you look for jobs, you certainly want to work for the “coolest” company around.


Now, I would like you to contemplate whether stereotyping all the Muslims (all 1.6 Billion of them) with terrorism would be wise.


 

Response To

More on “Model”s and Intelligence

 

 

 

 

Short Review of "How to create a Mind" by Ray Kurzweil and "On Intelligence" by Jeff Hawkins

 
 
 
Short Review on “How to create a Mind” [2] by Ray Kurzweil and “On Intelligence” [1] by Jeff Hawkins

#ArtificialIntelligence  #Neuroscience

What is Intelligence?
 
According to Jeff Hawkins, the hallmark of Intelligence is “prediction”.
 
But truly, Intelligence is
Knowledge about
  1. how the world works (including how my actions effect the world) and
  2. how to manage myself (so that I can take specific actions)
utlizing which I can achieve my goals.
 
 
How do you learn “how the world works”?
You learn how the world works by learning “Model”s of how different domains of the world work.
  • You learn the “Model” of how to use your Smartphone. Later, you apply the “Model” when you use a Tablet for the first time. 
  • You learn the “Model” of how Internal Combustion Engines work. Later you use the “Model” when you design a Car. 
  • The “Model” of how to best run an organization.
 
The more “Model”s you learn (each describing how a particular domain of the world works) the more Intelligent and Effective you become.
 
This definition of Intelligence includes prediction.
If you know how the world works, you can predict
“Given, this is what the current state of the world is, thatis what’s going to happen.”
 
“Hierarchical Temporal Memory” (HTM)
The model Jeff Hawkins presents in his book for making predictions –  “Hierarchical Temporal Memory” (HTM) is a definite step towards Artificial Intelligence.
Let’s see how hierarchies help us manage complexity.
 
Hierarchies for managing complexity
To manage complexity we use hierarchies.
  • We manage complexity in organizationsusing hierarchies (Top level Management, Mid-level management, low level management, etc.)
  • Similarly, we manage complexity in knowledge about the world using hierarchies.
As an example, 
a human face, if considered a high-level concept, 
the mid-level concepts include eyes, noses, etc. 
and low level concepts include cells, neurons, etc.
 
We organize our knowledge about Human Physiology using hierarchies :
-> Cellular organelles (DNA, Protein, Membrane, Robosomes, etc.) 
-> Cells 
-> Tissue 
-> Organ 
-> Body
 
So to understand how the world works (Go back to my definition of intelligence), we have to organize our knowledge of the world in hierarchies.
 
Intelligence requires Managing and Reasoning About Knowledge besides Hierarchical Representation
But the model Jeff describes is limited in the sense that hierarchical pattern recognition can only help you learn single step rules:
If an object has eyes and noses in such and such way,
then, it’s a face.
 
Intelligence is not just learning Associations or “Input -> Output”s.
Intelligence requires you to manage and reason about knowledge at a higher level.
Consider, the Search techniques used in AI. 
If A & B, Then C. 

If C, then D,

etc.
 
So hierarchical pattern recognition is a step towards intelligence but it’s limited in scope.
One needs high level “processes”
that utilize the learned “patterns”.
 
Repreresenting All Knowledge requires High Level Knowledge Management Besides Hierarchies
Hierarchical Pattern recognition is a step in the right direction but not sufficient for Knowledge Representation of the world.
One requires Knowledge Ontology and a few other high level tools to organize vast bodies of knowledge.
 
Other Hierarchical Models for pattern recognition

 

  • Hierarchical Hidden Markov Model  
    • Explained in the book “How to create a Mind [2] by Ray Kurzweil 
  • Deep learning (Hierarchical Neural Network) [3]
 
 
Hawkins’ model differs because it includes time – it’s a temporal model.
Hawkins observed that events happen in “time”.
If at a particular instant of this happens,
then this is what’s going to happen next.
So the hierarchical models of the world should include “time”. That’s where the Temporal in“Hierarchical Temporal Memory” (HTM) comes from.
 
 
The Secret!!
Now let me unveil the secret to my smarts.
I know what makes Intelligence works and I use those tools deliberately while I am thinking.That’s what makes me Intelligent and Effective.
 
 
 
Reference
  1. On Intelligence by Jeff Hawkins 
  2. How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil 
  3. Deep learning 

On Computer Science

My Articles On Artificial Intelligence and Data Sciences

Guidelines For Research In Neuroscience

Area Of Expertise: # Computational Neuroscience #NeuroEngineering  # Neuroscience

Guidelines For Research In Neuroscience:

Simulation is not enough (Specific cases),

Start building Models & Theories at successive levels of Abstractions (Generalizations).

  • Trying to understand the brain by creating exact models of the brain with enormous computing power (costing Billions of dollars) is not going to work – as is intended. 
    • We are never going to understand how the brain works completely with data only from the lowest level (molecules, channels, neurons) with so much complexity involved.
    • There is so much genetic variation from mouse to mouse, primate to primate, that you can’t draw general conclusions from data of genetic expression of a single mouse. What happens in the brain data scanning initiatives is that data is gathered from a single organism. And what is required is something similar to functional genomics – sort of functional neuroscience – trying to understand the relation between behavior and what happens in the brain – not just cataloging what data from a particular brain looks like.
    • What we need is new models, new theories, new abstractions – that can explain all these data.
    • We don’t need to simulate large parts of the brain on computers. Our goal should be simulation of small parts and theoretical models that can explain data from those small parts of the brain. 
  • We need to start building models, theories, abstractions. And then on top our first attempts at building models and theories, we will start building more accurate models, models that connect data from different levels of the brain. 
    • Our first attempts at building models, abstractions might concentrate on data from only one level. Next, our newer models would connect different levels of structural abstractions and their corresponding different levels of functional abstractions found in the brain.
    • Different structural levels of abstractions found in the brain:
      • Molecules, Receptors, Neurotransmitters. 
      • Neuron, Channels, Synapse, Glial Cells.
      • Collection of neurons
      • Brain regions (e.g., Primary Visual Cortex)
      • Brain – Behavior;  
  • Neuroscientists individually work on a tiny part at “only one level” (among all these levels, from molecules and neurons to whole brain) of the brain. We need scientists who can connect different levels of structural abstractions. 
  • The new breed of Neuroscientists, with the aim of building models, abstractions, theories of the brain, would try to learn how scientists with different backgrounds are studying Neuroscience.     
    • What diseases are Neurologists seeing in patients? How do the Neurologists explain them in terms of lesions, etc. in a particular brain region
    • Examples:
      • Speech – Broca’s area [2].
      • Synesthesia [3] – Cross-connections among nearby brain regions.
    • What diseases are Psychiatrists seeing in patients? How do they explain them in terms of excess or reduction in neurotransmitters? 
      • Examples: 
        • Schizophrenia – Excess of Dopamine [4]. 
    • Data from neurons, channels, molecules.
    • Data from specific brain regions (e.g., MRI, fMRI data).
    • Data from optogenetics – switching neurons on and off with light. 
    • Systems Neuroscience
    • Computer Models of brain. Connectomics.
    • What are we learning from our research in Artificial Intelligence about the requirements of intelligence? 
    • Cognitive Neuroscience – latest research on higher mental functions and brain. 
  • Psychologists have built models. Researchers interested in both Artificial Intelligence and Neuroscience (e.g., Marvin Minsky [1]) have built models. Why not start by trying to explain those models with our understanding of the brain?


Large Neuroscience Projects



References

Quora Question: How Can I Learn About Neurology On My Own? What Are Some Recommended Books To Read?

#BookRecommendation

The Science of Reading: Paper versus Screens

“Technology codes our minds,”
“A Magazine Is an iPad That Does Not Work”

When we read, we construct a mental representation of the text in which meaning is anchored to structure.

It is difficult to see any one passage in the context of the entire text.
The implicit feel of where you are in a physical book turns out to be more important than we realized. Only when you get an e-book do you start to miss it.
At least a few studies suggest that by limiting the way people navigate texts, screens impair comprehension.
“The ease with which you can find out the beginning, end and everything in between and the constant connection to your path, your progress in the text, might be some way of making it less taxing cognitively, so you have more free capacity for comprehension,” Mangen says.

Serendipity
People report that they enjoy flipping to a previous section of a paper book when a sentence surfaces a memory of something they read earlier, for example, or quickly scanning ahead on a whim.

Sense of control
People also like to have as much control over a text as possible—to highlight with chemical ink, easily write notes to themselves in the margins as well as deform the paper however they choose.

Because of these preferences—and because getting away from multipurpose screens improves concentration—people consistently say that when they really want to dive into a text, they read it on paper.

An emerging collection of studies emphasizes that in addition to screens possibly taxing people’s attention more than paper, people do not always bring as much mental effort to screens in the first place. Subconsciously, many people may think of reading on a computer or tablet as a less serious affair than reading on paper. Based on a detailed 2005 survey of 113 people in northern California, Ziming Liu of San Jose State University concluded that people reading on screens take a lot of shortcuts—they spend more time browsing, scanning and hunting for keywords compared with people reading on paper, and are more likely to read a document once, and only once.

When reading on screens, people seem less inclined to engage in what psychologists call metacognitive learning regulation—strategies such as setting specific goals, rereading difficult sections and checking how much one has understood along the way.

Jaejeung Kim of KAIST Institute of Information Technology Convergence in South Korea and his colleagues have designed an innovative and unreleased interface that makes iBooks seem primitive. When using their interface, one can see the many individual pages one has read on the left side of the tablet and all the unread pages on the right side, as if holding a paperback in one’s hands. A reader can also flip bundles of pages at a time with a flick of a finger.



Scrolling may not be the ideal way to navigate a text as long and dense as Moby Dick, but the New York Times, Washington Post, ESPN and other media outlets have created beautiful, highly visual articles that depend entirely on scrolling and could not appear in print in the same way. Some Web comics and infographics turn scrolling into a strength rather than a weakness. Similarly, Robin Sloan has pioneered the tap essay for mobile devices. The immensely popular interactive Scale of the Universe tool could not have been made on paper in any practical way. New e-publishing companies like Atavist offer tablet readers long-form journalism with embedded interactive graphics, maps, timelines, animations and sound tracks. And some writers are pairing up with computer programmers to produce ever more sophisticated interactive fiction and nonfiction in which one’s choices determine what one reads, hears and sees next.
– Notes taken from The Reading Brain in the Digital Age: The Science of Paper versus Screens, Scientific American.