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

Thursday, April 25, 2019

Episode 44: Can a Biologist Fix a Radio?

In 2002, cancer biologist Yuri Lazebnik raised and addressed the semi-facetious question "Can a biologist fix a radio?" in a short paper. The paper is a critique of current practices in the biological sciences, claiming they are inefficient at getting to truth. We discuss the stages of research progress in biological science Yuri describes, including the "paradoxical" stage where more facts leads to less understanding. We then dive into his view of how a biologist would approach a radio: describing what its parts look like, lesioning some of them, and making claims about what's necessary for the radio to work as a result. We reflect on how this framing of common biological research practices impacts our view of them and highlights how hard it is to understand complex systems. We talk about the (in)adequacy of Yuri's proposed solution to the problem (that biologists need to embrace formal, quantitative language) and the difference between engineering and science. Finally, we discuss a new take on this paper that goes through the effort of actually applying neuroscience methods to a microprocessor and the conclusions we took from that. Throughout we bring in specific examples from neuroscience we find relevant and Josh dismisses almost everything as "satirical".   

We read:
Can a Biologist Fix a Radio? - Or What I Learned Studying Apoptosis
Could a Neuroscientist Understand a Microprocessor?

And mentioned some topics covered in previous episodes:
Does Neuroscience Need More Behavior?
How Do We Study Behavior? 
Underdeterminacy and Neural Circuits

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)

Thursday, March 28, 2019

Episode 43: What Are Glia Up To?

Despite the fact that the brain is full of them, glial cells don't get much attention from neuroscientists. The traditional view of these non-neurons is that they are supportive cells---there to silently help neurons do what they need to do. On this episode we start by describing this traditional view, including types of glial cells and their roles. Then we get into the more interesting stuff. How do glia communicate with each other and with neurons? Turns out there are many chemical messages that get sent between these different cell types, including via the energy molecule ATP! We then talk about the ways in which these messages impact neurons and reasons why the role of glia may be hard for neuroscientists to see. In particular, glia seem to have a lot to say about the birth and control of synapses, making them important for scientists interested in learning. Finally we cover some of the diseases related to glia, such as multiple sclerosis and (surprisingly) depression. Throughout, we ask if glia are important for computation, and relatedly, how the hell do we define computation? Also Grace is weirded out that glia are everywhere but nobody is talking about (or drawing) them.

We read:
The Mystery and Magic of Glia
Glia: Listening and Talking to the Synapse

And mentioned some topics covered in previous episodes:
Understanding fMRI
Also, to hear more about special guest Nancy Padilla's research, check out our previous episode with her on Social Neuroscience

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, February 26, 2019

Episode 42: Learning Rules, Biological vs. Artificial

For decades, neuroscientists have explored the ways in which neurons update and control the strength of their connections. For slightly fewer decades, machine learning researchers have been developing ways to train the connections between artificial neurons in their networks. The former endeavour shows us what happens in the brain and the latter shows us what's actually needed to make a system that works. Unfortunately, these two research directions have not settled on the same rules of learning. In this episode we will talk about the attempts to make artificial learning rules more biologically plausible in order to understand how the brain is capable of the powerful learning that it is. In particular, we focus on different models of biologically-plausible backpropagation---the standard method of training artificial neural networks. We start by explaining both backpropagation and biological learning rules (such as spike time dependent plasticity) and the ways in which the two differ. We then describe four different models that tackle how backpropagation could be done by the brain. Throughout, we talk dendrites and cell types and the role of other biological bits and bobs, and ask "should we actually expect to see backprop in the brain?". We end by discussing which of the four options we liked most and why!

We read:
Theories of Error Back-Propagation in the Brain
Dendritic solutions to the credit assignment problem
Control of synaptic plasticity in deep cortical networks (we didn't discuss this one)

And mentioned several topics covered in previous episodes:
Reinforcement Learning
Predictive Coding
The Cerebellum
Neuromorphic Computing
Deep Learning

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)

Monday, January 28, 2019

Episode 41: Training and Diversity in Computational Neuroscience

This very special episode of Unsupervised Thinking takes place entirely at the IBRO-Simons Computational Neuroscience Imbizo in Cape Town, South Africa!

Computational neuroscience is a very interdisciplinary field and people come to it in many different ways from many different backgrounds. In this episode, you'll hear from a variety of summer school students who are getting some of their first exposure to computational neuroscience as they explain their background and what they find interesting about the field. In the second segment of the episode, we go into a conversation with the teaching assistants about what could make training in computational neuroscience better in the future and what we wish we had learned when we entered the field. Finally, we throw it back to the students to summarize the impact this summer school had on them and their future career plans.

We mentioned:
Our episode on Global Science

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) as is the transition music in this episode, titled "Artifact"

Wednesday, December 19, 2018

Episode 40: Global Science

In the past few years, we've noticed researchers making more explicit efforts to engage with scientists in other countries, particularly those where science isn't well-represented. Inspired by these efforts, we took a historical dive into the international element of science with special guest Alex Antrobus. How have scientists viewed and communicated with their peers in other countries over time? To what extent do nationalist politics influence science and vice versa? How did the euro-centric view of science arise? In tackling these issues, we start in the 1700s and work our way up to the present, covering the "Republic of Letters," the Olympic model of scientific nationalism, communism, and decolonization. We end by discussing the ethical pros and cons of mentoring and building academic "outposts" in other countries. Throughout, we talk about the benefits of open science, the King of Spain's beard, and how Grace doesn't do sports. 

We read:
A History of Universalism: Conceptions of the Internationality of Science from the Enlightenment to the Cold War
The Global Turn in the History of Science
The Development of Global Science

And mentioned:
IBRO-Simons Computational Neuroscience Imbizo
Deep Learning Indaba

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, November 29, 2018

Episode 39: What Does the Cerebellum Do?

Cerebellum literally means "little brain," and in a way, it has been treated as a second-class citizen in neuroscience for awhile. In this episode we describe the traditional view of the cerebellum as a circuit for motor control and associative learning and how its more cognitive roles have been overlooked. First we talk about the beautiful architecture of the cerebellum and the functions of its different cell types, including the benefits of diversity. We then discuss the evidence for non-motor functions of the cerebellum and why this evidence was hard to find until recently. During this, we struggle to explain what cognitive issues someone with a cerebellar lesion may have and special guest/cerebellum expert Alex Cayco-Gajic tests our cerebellar function. Finally, we end by lamenting the fact that good science is impossible and Alex tells us how the future of neuroscience is subcortical!

We read:
What the Cerebellum Computes
The Cerebellum and Cognitive Function: 25 Years of Insight from Anatomy and Neuroimaging

And mentioned:
Our Reinforcement Learning episode
eLife paper on learning a series of blinks
Sam Wang's review of autism and cerebellar damage
Paper on bird cerebellar pathways
fMRI (check out our episode on it)
Optogenetics (check out our episode on it)

And keep an eye out for Alex's upcoming review on pattern separation!
UPDATE: Here it is!

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) 

Sunday, October 28, 2018

Episode 38: Reinforcement Learning - Biological and Artificial

Reinforcement learning is important for understanding behavior because it tells us how actions are guided by reward. But the topic also has a broader significance---as an example of the happy marriage that can come from blending computer science, psychology and neuroscience. In this way, RL is a poster child for what's known as Marr's levels analysis, an approach to understanding computation that essentially asks why, how, and where. On this episode we first define some of the basic terms of reinforcement learning (action, state, environment, policy, value). Then we break it down according to Marr's three levels: what is the goal of RL? How can we (or an artificial intelligence) learn better behavior through rewards? and where in the brain is this carried out? Also we get into the relationship between reinforcement learning and evolution, discuss what counts as a reward, and try to improvise some relatable examples involving cake, cigarettes, chess, and tomatoes.

We read:
Reinforcement Learning with Marr
Reinforcement learning: Computational theory and biological mechanisms

And mentioned:
Understanding dopamine and reinforcement learning: The dopamine reward prediction error hypothesis - by Paul Glimcher
Training a glider with RL

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)