Unsupervised Thinking

As computational neuroscientists, the members of this podcast take for granted the fact that math can be used to describe the brain. But is this ability of math trivial or deeply surprising? On this episode we tackle the large philosophical issues behind computational sciences, with the help of a set of articles entitled "The Unreasonable Effectiveness of Mathematics in the Natural Sciences" (1960) and "The Unreasonable Effectiveness of Mathematics" (1980). These articles, written by mathematicians, claim that the ability of mathematics to explain the world is unexpectedly impressive. This leads us to a set of questions: What is math? What is the world? And how do we know what to expect from either? Is this ability of math truly unreasonable or did it just appear so at a certain time? As we explore these ideas we discuss thought experiments involving artificial intelligence and real experiments involving the Cold War. We also hypothesize on the role of elegance in mathematics and what "understanding" really means to us as humans.

The body is generally kept at around 37 degrees C, and the brain maybe even a degree higher. So it might seem like a bad idea to cool it down. But against intuition (or with it?) freezing the brain can actually be helpful, both for science and for medicine. In this episode we explain how cooling regions of the brain is used to create temporary lesions that allow scientists to explore their function. We also get into the tools scientists use to achieve this. We then cover more modern experiments, wherein specific cooling has given insights on key aspects of the visual system, bird song production, and even human speech production. Finally, we get into the medical uses of cooling including how it can be used to fight long-term effects of traumatic brain injury. Throughout, we hit on the absurdities of brain region naming, how rats feel about research, and some wild speculation about ancient Egyptians.

The connectome--a silly word and a somewhat simple concept that has caused significant debate amongst neuroscientists worldwide. In this episode, we teach the controversy surrounding the big C. First, we trudge through a definition of the connectome (it means different things to different people, but generally centers on mapping out what's connected to what in the brain), and methods used to obtain it. We then talk about what we've learned from the only animal for which we have the full connectome: the worm C. elegans. Then, via a comparison to the Human Genome Project (inspiration for the word "connectome"), we ask whether neuroscientists believe it's worth the money and the collective effort of the field to pursue the connectome in other animals. This leads us onto a tangent regarding how the public views neuroscience and whether it's morally acceptable to oversell work in order to gain funding. Ultimately, we end in a state of uncertainty, much like the field of neuroscience itself.

It's the big 1-0 here at Unsupervised Thinking and for it we are tackling a big topic: brain size! [insert obligatory "does size matter" joke here]. Ever since humans started using their intelligence to compare their brains to that of other animals, we've been searching for what makes us so much more intelligent than those other animals. In this episode we ask are we so much more intelligent? And if so is it due to our brain mass? volume? number of neurons? After summarizing how these properties vary across species, we get into the evolutionary pressures that would lead to big brains and how scientists study brain size today. Finally, we look within humans to see how brain size varies across people and whether it's correlated with intelligence. Throughout we learn that Conor is freaked out by the fact that whales are mammals in water and Josh has some really weird ideas about fabric.

Do you know what happens when scientists patent their ideas? Do you know who gets the money from those patents? Did you know that scientists even patent things at all? We weren't so sure about these topics back in our Optogenetics episode, so we brought in Penn Law alum/fellow neuroscience grad student Jozsef Meszaros to tell us about this interesting and under-covered topic for scientists. We ask if academics are allowed to use patented objects without a licence, who owns the rights to a patent when research is publicly-funded, how do scientists feel about the patent system, and why does the patent system even exist? We also work through who benefits and who is getting shafted in this system between scientists, universities, tax-payers, the government, and corporations.

In some ways, neuroscience is like psychology's little brother. It came around later, started encroaching on psychology's turf and drawing attention to itself, and so they don't always get along. On this episode we explore the place where neuro and psych interact, and the separate roles for each. We embarked on this after reading an article written by psychologists, wherein they describe their perspective on how neuroscience is affecting the field of psychology. We cover our opinions on the differences between these two fields, the philosophical basis for these differences, and how that manifests into practical considerations. Specifically we talk hiring practices, disease treatment and "neuro-seduction". At the heart of all this is the question: on what level should we approach the study of the brain and mind? and who are we to decide?

Inside a skull it's generally pretty dark. But about 10 years ago, as part of a new method for toying with neurons, scientists started shining a light in there. That method is optogenetics, and it allows for highly-specified control of neural activity by light, via genetic engineering. As part of the 10th anniversary, we cover the development and impact of this influential technology. With help from this review article, we talk about the scientific atmosphere when optogenetics was unveiled and what allowed it to take off so quickly. Then we get into the mechanisms of how neurons come to be controlled by light (and we stumble upon a fantastic government conspiracy theory along the way). Finally we discuss what has been learned by applying optogenetics for the past 10 years, what we could possibly expect to learn, and the possibility of therapeutics based in this technology.

tDCS is old school brain manipulation involving electrodes and sponges. For our 6th episode we describe the basic premise of tDCS (really simple) and its effects on the brain (really not simple). We also talk about its use as a treatment of depression and other clinical applications, which leads us into a meaty digression on clinical science and whether the methods used for discovering medical treatments make any goddamn sense. Finally, we speak of tDCS's scientific uses for understanding the role of various brain areas, and if any of us would ever try it on ourselves.

For our 5th episode, we get into braaaiiiiinnnwaaaaaavessss. By which we mean neural oscillations. By which we really mean a lot of different things it turns out. For this, we bring in special guest Nancy Padilla, who actually puts electrodes into animals to study these things. We define the vocabulary of the field and then Nancy tells us how she uses these measurements for her own work. Then with the help of this paper, we get into what we think can reasonably be concluded from extracellular-oscillation style studies, and the seemingly seductive nature of oscillations to explain everything. All throughout you're gonna hear a lot about LFPs (local field potentials), including Conor's lament about their undefinable nature. And Josh is going to demand that Nancy explain how oscillations could be of use to us computational types. Finally we wrap up with a bit of redemption and common ground, surrounding this paper on "ephaptic coupling".

As you may have heard, there's been a lot of talk about something called deep learning lately. So for our fourth episode, we and our special guest, Ryan Tombacco, are taking a deep dive into deep learning! We give broad overviews of machine learning, artificial intelligence, and the historical foundations of deep learning and then we get into some nitty gritty examples of what deep learning can do today. All throughout Ryan uses his podcast-ready voice to ask some really insightful questions, and we end up musing on the relationships between deep learning and the brain along with some philosophy of learning (Josh even brings up Kant...)