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

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 or use the player below





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) 

Tuesday, September 25, 2018

Episode 37: What is an Explanation? - Part 2

In part two of our conversation on what counts as an explanation in science, we pickup with special guest David Barack giving his thoughts on the "model–mechanism–mapping" criteria for explanation. This leads us into a lengthy discussion on explanatory versus phenomenological (or "descriptive") models. We ask if there truly is a distinction between these model classes or if a sufficiently good description will end up being explanatory. We illustrate these points with examples such as the Nernst equation, the Hodgkin-Huxley model of the action potential, and multiple uses of Difference of Gaussians in neuroscience. Throughout, we ask such burning questions as: can a model be explanatory if the people who made it thought it wasn't? are diagrams explanations? and, is gravity descriptive or mechanistic?

Listen to Part One first!

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

And David mentioned: Craver's Explaining the Brain


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) 

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

UPDATE: Part 2 is now posted here


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