Unsupervised Thinking
a podcast about neuroscience, artificial intelligence and science more broadly

Wednesday, August 29, 2018

Episode 36: What is an Explanation? - Part 1

As scientists, we throw around words like "explanation" a lot. We assume explaining stuff is part of what we're doing when we make and synthesize discoveries. But what does it actually take for something to be an explanation? Can a theory or model be successful without truly being one? How do these questions play out in computational neuroscience specifically? We bring in philosopher-neuroscientist David Barack to tackle this big topic. In part one of the conversation, David describes the historical trajectory of the concept of "explanation" in philosophy. We then take some time to try to define computational neuroscience, and discuss "computational chauvinism": the (extremist) view that the mind could be understood and explained independently of the brain. We end this first half of the conversation by defining the "3M" model of explanation and giving our initial reactions to it.

We read:
Explanation and description in computational neuroscience
(which was a recommendation from this Twitter thread on the philosophy of mathematical modeling)

Previous episodes that may be of interest:
Episode 8: Neuroscience vs. Psychology
Episode 13: The Unreasonable Effectiveness of Mathematics
Episode 21: Understanding fMRI

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) 

Tuesday, July 31, 2018

Episode 35: Generative Models

Machine learning has been making big strides in a lot of straightforward tasks, such as taking an image and labeling the objects in it. But what if you want an algorithm that can, for example, generate an image of an object? That's a much vaguer and more difficult request. And it's where generative models come in! We discuss the motivation for making generative models (in addition to making cool images) and how they help us understand the core components of our data. We also get into the specific types of generative models and how they can be trained to create images, text, sound and more. We then move onto the practical concerns that would arise in a world with good generative models: fake videos of politicians, AI assistants making our phone calls, and  computer-generated novels. Finally, we connect these ideas to neuroscience, asking both how can neuroscientists make use of these and is the brain a generative model?

We read:
OpenAI blog
MIT Tech Review Article 
(accidentally referred to as Wired article...oops!)

And skimmed/mentioned:
Episode 4 - Deep Learning
Another good overview of some generative models
Google Assistant making phone call
Uses of generative models in neuroscience 
Episode 33 - Predictive Coding

And our special guest was Yann Sweeney!

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) 

Thursday, June 28, 2018

Episode 34: The Gut-Brain Connection

Because of the sheer number of neurons in the gut, the enteric nervous system is sometimes called the second brain. What're all those neurons doing down there? And what, or who, is controlling them? Science has recently revealed that the incredibly large population of microorganisms in the gut have a lot to say to the brain, by acting on these neurons and other mechanisms, and can impact everything from stress to obesity to autism. In this episode, we give the basic stats and facts about the enteric nervous system (and argue about whether it really is a "second brain") and cover how the gut can alter the brain via nerves, hormones, and the immune system. We then talk about what happens when mice are raised without gut microbes (weird) and whether yogurt has any chance of curing things like anxiety. Throughout, we marvel at how intuitive all this seems despite being incredibly difficult to actually study. All that plus: obscure literary references, Josh's hilariously extreme fear of snakes, multiple misuses of the word "species," and DIY feces transplants! 

We read:
Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour

And mentioned:
Nancy's previous episode Episode 31: Consuming Science
Episode 23: What Can Neuroscience Say About Consciousness?

And our special guest was Nancy Padilla!

To listen to (or download) this episode, (right) click here
[apologies for minor audio issues on this one!]

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

Wednesday, May 30, 2018

Episode 33: Predictive Coding

You may have heard of predictive coding; it's a theory that gets around. In fact, it's been used to understand everything from the retina to consciousness. So, before we get into the details, we start this episode by describing our impressions of predictive coding. Where have we encountered it? Has it influenced our work? Why do philosophers like it? And, finally, what does it actually mean? Eventually we settle on a two-tiered definition: "hard" predictive coding refers to a very specific hypothesis about how the brain calculates errors, and "soft" predictive coding refers to the general idea that the brain predicts things. We then get into how predictive coding relates to other theories, like Bayesian modeling. But like Bayesian models, which we've covered on a previous episode, predictive coding is prone to "just-so" stories. So we discuss what concrete predictions predictive coding can make, and whether the data supports them. Finally, Grace tries to describe the free energy principle, which extends predictive coding into a grand unified theory of the brain and beyond.

We read:
Whatever next? Predictive brains, situated agents, and the future of cognitive science

And mentioned:
Episode 15: "Just-so" stories in Bayesian modeling in psychology
Kok and Lange book chapter on predictive coding
NYU panel/debate on predictive coding

And our special guest was Alex Cayco-Gajic!

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) 

Tuesday, May 1, 2018

Episode 32: How Do We Study Behavior?

There is a tension when it comes to the study of behavior in neuroscience. On the one hand, we would love to understand animals as they behave in the wild---with the full complexity of the stimuli they take in and the actions they emit. On the other hand, such complexity is almost antithetical to the scientific endeavor, where control over inputs and precise measurement of outputs is required. Throw in the constraints that come when trying to record from and manipulate neurons and you've got a real mess. In this episode, we discuss these tensions and the modern attempts to resolve them.

First, we take the example of decision-making in rodents to showcase what behavior looks like in neuroscience experiments (and how strangely we use the term "decision-making"). In these studies, using more natural stimuli can help with training and lead to better neural responses. But does going natural make the analysis of the data more difficult? We then talk about how machine learning can be used to automate the analysis of behavior, and potentially remove harmful human biases. Throughout, we provide multiple definitions of "behavior", Grace relates animal training to parenting, and our special guest Adam Calhoun uses his encyclopedic knowledge of this area to provide many insightful examples!

We read:
Decision making behaviors: weighing ethology complexity and sensorimotor compatibility
Computational Analysis of Behavior - Annual Review of Neuroscience [$]

And mentioned:
Episode 18: Does Neuroscience Need More Behavior?
Mala Murthy (fly courtship work)
Low dimensionality of C. elegans (worm) behavior
How sensory neural responses are heavily influenced by behavior

For more, check out this list of behavioral papers Adam made on Twitter!

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) 

Saturday, April 7, 2018

Episode 31: Consuming Science (with Cosyne Interviews)

On this unique episode of Unsupervised Thinking, we talk not about a particular area of science, but about the process of doing science itself. In particular, we're discussing how scientists take in information from their niche research areas and beyond. The topic for this free-form conversation stemmed from interviews we collected at the latest Computational and Systems Neuroscience Conference (Cosyne), where we asked people to tell us about a research finding from outside their area that they thought was cool. You'll hear those interviews in this episode, along with our motivation for asking that question and our reaction to the responses. We then go on to speak broadly about our experiences at different conferences both big and small. In particular, we reveal how attending talks from far-reaching areas of science is a great way to build appreciation for your field and contextual your research. Ultimately, influence from talks and colleagues is how scientists choose their projects, and so decisions of what to consume can have long-lasting effects. We give our personal examples of times when talks have unexpectedly impacted our research, and the concrete things we do to keep up with the literature. Throughout, there is also talk of the culture divides that arise in research and Josh tells us about how Game of Thrones special effects are done. Thanks to all the Cosyne attendees who agreed to be interviewed for this, and special thanks to Nancy Padilla, our special guest for this episode!

We mentioned:
Episode 5: Neural Oscillations
Episode 24: Social Neuroscience Research

Also relevant, Grace wrote a blog on some of these themes for the Simons Foundation: Cross-Pollination at Cosyne 2018

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) 

Tuesday, April 3, 2018

Interviews from Cosyne 2018

At the most recent Computational and Systems Neuroscience Conference ("Cosyne"), held in Denver, we collected some interviews from attendees. The goal was to get people talking about work outside of their own immediate research area. Cosyne is a great conference to get exposed to ideas from other areas, particularly across the experiment-theory boundary.

In total we collected 11 interviews from grad students, postdocs, and professors. We prompted people by asking them to first give their name, position, and describe a bit about their own work. We then asked them to tell us about a finding or research method (not necessarily from the conference, though that was common) that is outside their own area of work that they thought was cool/exciting/interesting. Listen to all these interviews here:

HTML5 Audio Player

Based on these interviews and the general Cosyne experience, Grace wrote a column for the Simons Foundation blog on how small, single-track conferences can foster scientific creativity. You can check that out here: Cross-Pollination at Cosyne 2018

These interviews will also be incorporated into our upcoming episode, where we'll have an informal chat about the difficulties and benefits of keeping up with the broad scientific literature.