Models of the Mind: How physics, engineering, and mathematics have shaped our understanding of the brain

Grace wrote a book! And she talked to Brain Inspired host Paul Middlebrooks about it.

 

The book is about the many different ways mathematical methods have influenced neuroscience, from models of single cells all the way up to equations to explain behavior. You can learn more about the book and how to get it in e-book, audiobook, and hard cover worldwide here! 

On this cross-posting of Brain Inspired, Grace talks about the book and the field of computational neuroscience more generally. Give it a listen and go check out other episodes of Brain Inspired for more great conversations. 

The episode also contains a short intro from Grace about the state of Unsupervised Thinking (hint: it's not coming back 😥).

To listen to (or download) this episode, (right) click here or use the player below

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com)

Licensed under Creative Commons: By Attribution 3.0

Unsupervised Thinking is on hiatus!

As some listeners may know, UST co-host Grace Lindsay is writing a book. That book---a popular science take on the science, history, and philosophy of many different topics in computational neuroscience---is very nearly due to the publisher. As a result, time for podcasting (or really anything other than book writing...) is rapidly disappearing!

So until further notice, there will be no new episodes of Unsupervised Thinking :( But please enjoy our catalogue of over 50 episodes covering neuroscience, artificial intelligence, and science more broadly!

Episode 51: Motor Control

To some neuroscientists, the brain exists to produce movement and everything else it does should be understood in light of this goal. On this episode, we talk about these "motor chauvinists" and the broader topic of how motor control is studied in neuroscience and artificial intelligence. First we briefly discuss the tangled anatomy of motor control in animals. Then we get into how artificial motor control is done, including optimal feedback control, reinforcement learning, and the six core principles of hierarchical motor control. Finally we relate these principles back to the biology and talk about what the future of the study of motor control needs. Throughout we conduct an experiment on ourselves, reflect on what makes motor outputs different from other tasks, and hear what Alex thinks is undeniably true or undeniably false about the motor system.

We read:
Descending pathways in motor control
Hierarchical motor control in mammals and machines

Also of interest:
This TED talk that explains the sea squirt example (which we call a sponge in the episode...)
Paper Alex mentioned about plasticity in spinal cord 

Alex's new *faculty* website can be found here: https://sites.google.com/view/caycogajic/home

And we mentioned previous episodes/topics:
Episode 48: Studying the Brain in Light of Evolution
Episode 47: Deep Learning to Understand the Brain 
Episode 39: What Does the Cerebellum Do? 
Episode 38: Reinforcement Learning - Biological and Artificial


To listen to (or download) this episode, (right) click here or use the player below

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com)

Licensed under Creative Commons: By Attribution 3.0

Episode 50: Brain Organoids

Most neuroscience research takes place in a full, live animal. But brain organoids are different. Brain organoids are three-dimensional blobs of brain grown from human stem cells and they offer novel access to the study of human brain development. On this episode we go beyond our computational comfort zone to talk about the history of stem cells, the potion of chemicals needed to get these little blobs to grow, and the extent to which they mimic features of the human brain when they do. We also discuss the promise of studying and treating disease through personalized organoids, and how this gets hard for higher level disorders like schizophrenia. Then we get into questions of embodiment and if giving these organoids more means to interact with the world would make them better models of the brain and of information processing. Finally we get to the ethics of it all, and find that bioethicists these days are actually chill AF. Throughout, we find out that Josh is not surprised by any of this, and we tackle the pressing moral issue of our time: is it OK to eat your thermostat?

We read:
Brain Organoids: Expanding Our Understanding of Human Development and Disease
Cortical organoids: why all this hype?
The Ethics of Brain Organoids
Organoids Are Not Brains. How Are They Making Brain Waves?

And we mentioned previous episodes/topics:
Episode 46: What We Learn from Model Organisms 
Episode 43: What Are Glia Up To?


To listen to (or download) this episode, (right) click here

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com)

Licensed under Creative Commons: By Attribution 3.0

Episode 49: How Important is Learning?

The age-old debate of nature versus nurture is now being played out between artificial intelligence and neuroscience. The dominant approach in AI, machine learning, puts an emphasis on adapting processing to fit the data at hand. Animals, on the other hand, seem to have a lot of built in structure and tendencies, that mean they function well right out of the womb. So are most of our abilities the result of genetically-encoded instructions, honed over generations of evolution? Or are our interactions with the environment key? We discuss the research that has been done on human brain development to try to get at the answers to these questions. We talk about the compromise position that says animals may be "born to learn"---that is, innate tendencies help make sure the right training data is encountered and used efficiently during development. We also get into what all this means for AI and whether machine learning researchers should be learning less. Throughout, we ask if humans are special, argue that development can happen without learning, and discuss the special place of the octopus in the animal kingdom.

Follow special guest Alex Antrobus on twitter! 

We read:
Functional Brain Development in Humans
A critique of pure learning and what artificial neural networks can learn from animal brains
Weight Agnostic Neural Networks

And we mentioned previous episodes/topics:
Global Science
Training and Diversity in Computational Neuroscience
Studying the Brain in Light of Evolution


To listen to (or download) this episode, (right) click here

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com)

Licensed under Creative Commons: By Attribution 3.0

Episode 48: Studying the Brain in Light of Evolution

The brain is the result of evolution. A lot of evolution. Most neuroscientists don't really think about this fact. Should we? On this episode we talk about two papers---one focused on brains and the other on AI---that argue that following evolution is the path to success. As part of this argument, they make the point that, in evolution, each stage along the way needs to be fully functional, which impacts the shape and role of the brain. As a result, the system is best thought of as a whole---not chunked into perception, cognition and action, as many psychologists and neuroscientists are wont to do.  In discussing these arguments, we talk about the role of representations in intelligence, go through a bit of the evolution of the nervous system, and remind ourselves that evolution does not necessarily optimize. Throughout, we ask how this take on neuroscience impacts our own work and try to avoid saying "represents".

We read:
Resynthesizing behavior through phylogenetic refinement
Intelligence without Representation


And we mentioned previous episode topics:
The Concept of Coding
Reinforcement Learning, Biological and Artificial


To listen to (or download) this episode, (right) click here or use the player below

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com)

Licensed under Creative Commons: By Attribution 3.0

Episode 47: Deep Learning to Understand the Brain

The recent advances in deep learning have done more than just make money for startups and tech companies. They've also infiltrated neuroscience! Deep neural networks---models originally inspired by the basics of the nervous system---are finding ever more applications in the quest to understand the brain. We talk about many of those uses in the episode. After first describing more traditional approaches to modeling behavior, we talk about how neuroscientists compare deep net models to real brains using both performance and neural activity. We then get into the attempts by the field of machine learning to understand their own models and how ML and neuroscience can share methods (and maybe certain cultural tendencies). Finally we talk about the use of deep nets to generate stimuli specifically tailored to drive real neurons to their extremes. Throughout, we notice how deep learning is "complicating the narrative", ask "are deep nets normative models?", and struggle to talk about a topic we actually know about.  

We read:
Deep neural network models of sensory systems: windows onto the role of task constraints
Analyzing biological and artificial neural networks: challenges with opportunities for synergy?
Neural population control via deep image synthesis
Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences

And we mentioned previous episodes:
Deep Learning
"Just-So" Stories of Bayesian Modeling in Psychology
Learning Rules, Biological vs. Artificial
Grace has also written a blog on comparing CNNs to the visual system:
Deep Convolutional Neural Networks as Models of the Visual System: Q&A


Finally, for those who get to the end of the episode, these are the images we're talking about (you decide which ones are pleasant and which are creepy AF....):
 



To listen to (or download) this episode, (right) click here

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com)

Licensed under Creative Commons: By Attribution 3.0