Episode 46: What We Learn from Model Organisms

From worms to flies, and mice to macaques, neuroscientists study a range (but not very large range...) of animals when they study "the brain". On this episode we ask a lot of questions about these model organisms, such as: how are they chosen? should we use more diverse ones? and what is a model organism actually a model of? We also talk about how the development of genetic tools for certain animals, like mice, have made them the dominant lab animal and the difficulty of bringing a new model species onto the scene. We also get into the special role that simple organisms, like C. elegans, play and how we can extrapolate findings from these small animals to more complex ones. Throughout, special guest Adam Calhoun joins us in asking "What even is the purpose of neuroscience???" and discussing the extent to which mice do or do not see like humans. 

We read:

The emperor’s new wardrobe: Re-balancing diversity of animal models in neuroscience research
Model organisms: new kids on the block
The fruits of fly research
C. elegans: a model system for systems neuroscience

And here are some other readings on the topic:
Non-mammalian models in behavioral neuroscience: consequences for biological psychiatry
100 years of Drosophila research and its impact on vertebrate neuroscience: a history lesson for the future
Hail Hydra! Doing neuroscience without a brain
Special Issue: Contributions from different model organisms to brain research
Adam's Twitter thread

And we mentioned previous episodes:
How Do We Study Behavior?
The Connectome
Related as well:
Does Neuroscience Need More Behavior?



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 45: How Working Memory Works

Working memory is the ability to keep something in mind several seconds after it's gone. Neurons don't tend to keep firing when their input is removed, so how does the brain hold on to information when it's out of sight? Scientists have been probing this question for decades. On this episode, we talk about how working memory is studied and the traditional view of how it works, which includes elevated persistent firing rates in neurons in the prefrontal cortex. The traditional view, however, is being challenged in many ways at the moment. As evidence of that we read a "dueling" paper on the topic, which argues for a view that incorporates bursts of firing, oscillations, and synaptic changes. In addition to covering the experimental evidence for different views, we also talk about the many computational models of working memory that have been developed over the years. Throughout we talk about energy efficiency, the difference between maintenance and manipulation, and the effects of putting scientific disagreements in writing. We also admit to not reading *any* primary sources.

We read:
Persistent Spiking Activity Underlies Working Memory
Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not
Working models of working memory


And mentioned previous episodes:
Bayesian Modeling in Psychology 
Brain Freezing and Cooling
Neural Oscillations

And some other work:
Ring attractor for heading direction in flies
Neuronal circuits underlying persistent representations despite time varying activity


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 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)

Licensed under Creative Commons: By Attribution 3.0

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:
Optogenetics
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)

Licensed under Creative Commons: By Attribution 3.0

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)

Licensed under Creative Commons: By Attribution 3.0

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"

Licensed under Creative Commons: By Attribution 3.0

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) 

Licensed under Creative Commons: By Attribution 3.0