Unsupervised Thinking

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

Sleep is such a ubiquitous part of our lives we may forget just how weird of a thing it is to spend a third of our days laying in darkness. In this episode on the science of sleep, we start by describing types of sleep (while appreciating its strangeness) and the negative cognitive effects of missing out on it. We also discuss the potential role of rapid eye movement (REM) sleep in training neural connections and how that idea has been ported to artificial intelligence. We then take a rare (for us) trip to the brain stem to understand the neural mechanisms of switching from wake to sleep and nonREM to REM sleep. While there we appreciate how difficult it is to tease apart the function of different neuron types in a heavily redundant system. Finally, we dip our toes into the world of dreams, discussing a theory of dreaming's role in developing consciousness and our own experiences with lucid dreaming. Throughout, we go down tangents to argue about the value of speculative science and how to quantify knowledge

In the late 19th century, many of the most basic foundations of neuroscience were laid, but not without a fight. In this episode, we cover the famous debate between Camillo Golgi and Santiago Ramon y Cajal regarding the very nature of neurons: are they separate cells or one conjoined mass? We also tell the story of some of the lesser known players, remark on the inability of those on the losing side to let go, and ask what a modern Neuron Doctrine debate would look like. After covering the history, we discuss two modern rethinkings of the Neuron Doctrine that focus the question not just on anatomy, but on whether neurons are the functional unit of the brain. What does "functional unit" mean? We don't know, but as we grapple with it we speak about the need to balance simplification with appreciation of details and (unfortunately) attempt to understand emergence again.

In this very special episode of Unsupervised Thinking, we bring together a group of neuroscientists and neuroscience enthusiasts to have a semi-structured discussion on the past, present, and future of the field of neuroscience. The group includes your three regular hosts plus Yann, Alex, and Ryan (whose voice you may recall from our Deep Learning episode) and we each give our thoughts on what got us into neuroscience, what we feel the field is lacking, and where the field will be in 20 years. This leads us on a path of discussing statistics, emergence, religion, depression, behavior, engineering, society, and more!

In the second part of our discussion on the concept of coding, we get into specific examples of what could count as the "neural code". In particular, we talk rate vs temporal coding along with examples of each in different organisms. We then get into the use of coding as a metaphor. How does it influence thought and experiments? Is an "action" or "process" metaphor a better alternative? We also discuss why language is the wrong metaphor for the neural code and the relationship between metaphors and formal mathematical systems. To illustrate the tension between a coding framework versus an action/process framework, we discuss a concrete example where the same finding is explained two different ways: does orientation-selective cells in primary visual cortex arise from sparse coding principles or is it just what happens when you train a system to do vision? We read: Neural Representation and the Cortical Code Is coding a relevant metaphor for the brain?

The concept of "coding," along with language referencing it, is abundant in neuroscience whether describing sensory systems, cognition, or motor control. The notion that neurons encode information is so core to neuroscience it is almost difficult to imagine the field without it. In the first part of this two-part discussion on coding, we talk about the origins of the coding concept. We start with some of the early experimental work that demonstrated the most basic response properties of neurons. We then delve into a conference report from the 60's that summarized the state of the field at that time and find it (depressingly?) relevant for today. In particular, the focus at that time on coding as only an imperfect metaphor is contrasted with its perhaps outsized role in modern work. We ask things like: When is the metaphor working, and when is it stretched beyond recognition? What qualities does a code need to have to be a candidate for the "neural code"? and If there is a neural code, who's reading it out? By

You move your eyes several times a second, making choices about what to attend to without even noticing. That is a lot of behavioral data that scientists could use to understand underlying computations, preferences, memories, and intentions. On this episode, we talk about just such endeavors to understand cognition by monitoring eye movements. Eye movements are a comparatively easy thing to measure (though as we discuss, some of the older methods seemed like torture devices), and can be used in a wide range of settings: in animals and babies, in the lab or in the wild. In this episode, Josh regals us with tales of using a modern eye-tracking device at a conference, and we talk about the basic findings you can discover from that and exactly how surprising or interesting they are. We then get into how tasks influence eye movements and the many forms of memory that eye movements can measure, including differences between novices and experts. We top it all off with a delightful study about looking at porn, and so

For this episode, special guest Nancy Padilla (E5: Neural Oscillations) returns to talk about a topic she now studies: social neuroscience. We get into the methods this rather new field uses to probe the neural processes behind social interaction, including the inherent difficulties in studying such a complex subject. We go from special pathways for bottom-up social processing such as smells and facial recognition to ideas about theory of mind and cooperation. In the process, we hit on the mirror neuron system, simultaneous dual-brain recordings, and the role of philosophy in the field. Two common questions throughout are: (1) are humans unique amongst animals? and (2) is social processing unique amongst neuroscience topics? Ultimately, we try to discover if social neuroscience is greater than the sum of its parts. We read: Conceptual Challenges and Directions for Social Neuroscience Brain Basis of Human Social Interaction: From Concepts to Brain Imaging Brain-to-Brain coupling: A mechanism for creating a

For this episode, we try to de-thorn one of the thorniest topics in neuroscience: consciousness! Starting with the (not so) simple task of defining the c-word, we first lay out our own definitions but end up relying on the work of philosopher David Chalmers (easy and hard problems of consciousness, philosophical zombies). Then, after establishing the myriad of reasons why science can't actually study consciousness, we get into its attempts to do so. Specifically, we talk about studies on anaesthesia and the role of optical illusions in probing conscious perception. After that we go through a laundry list of all kinds of theories/models of consciousness put forth by neuroscientists, psychologists, and (ugh) physicists. Throughout, Josh complains about people saying they have "heightened consciousness", Conor advocates a lot of drug use (for science), and we discuss the politics of urination. If you're confused by any of this, no worries, Conor is too. We read: Scholarpedia: Neuronal Correlates of Consciousn

Sloppiness, stiffness, and stomatogastric ganglion! This episode on underdeterminacy in neural circuits will introduce you to all these topics, as well as to special guest Alex Williams! To start, we take you way back to algebra class with a refresher on what makes a system "underdetermined" (essentially, more unknowns than constraints). There are two ways this can be a problem in neuroscience: (1) neural circuit modelers don't have enough data to constrain their models, and (2) biology itself is underconstrained, leading to differences across individuals within a species. We talk about both of these issues separately, the ways in which they interact, and the practical effects they have for the study of the nervous system. The first topic spurs a broad discussion on the philosophy of modelling and the potential pitfalls that careful scientists need to avoid. To explore this in more detail, we discuss an excellent modelling paper on the oculor-motor system that demonstrates ways in which models should guide ex

To much of the world, the face of neuroscience is an image of a brain with small colored blobs on it. Those images come from functional magnetic resonance imaging (fMRI), a technology that's made a big splash in its relatively short tenure. For this episode, we delve into fMRI and what scientists do with the data it produces. To start, we review the technology behind MRI and fMRI. We get into the thorny issue of relating the BOLD signal recorded from fMRI with actual neural activity, and what's been learned from animal studies that have looked at both simultaneously. After that we talk stats: particularly the trouble with traditional "voxel"-wise methods and putting all your eggs in one basket (or in separate, but similar, baskets?). Approaches to fMRI analysis are quickly evolving however, and so we discuss multi-voxel pattern analysis, comparing across individual brains, real time-analysis, mind reading, and lie detecting. Finally, we turn a little more philosophical and ask "What does it mean to measure in

Sometimes scientists decide to turn their tools of inquiry inward to understand their own fields and behaviors. For our 20th episode, we're diving into this meta-science by reading some papers about by papers written by scientists studying scientists. In particular, we start with a commentary discussing the increasing size of scientific teams, and what that means for credit assignment. Do we need to move to a more Hollywood approach by highlighting specific achievements in different roles? Also, when will we address the fact that most young researchers on these teams will not have a career in academic science? We then get into a modeling study that aims to show how incentivizing the publication of novel results can ultimate lead to a widespread decrease in scientific quality. This raises questions of whether individuals or the system is to blame for high rates of shoddy publications. We then touch on a small experiment that the conference NIPS (Neural Information Processing Systems) performed on their peer re

Way back in Episode 16 we paired up with Always Already to talk about a book on gender and the interaction of science and society. Unsurprisingly, that conversation spanned far beyond the scientific study of gender and so we never really got into the biological weeds. Our intent with this episode was to go back to gender, with a focus on the explaining the current state of the science. What we quickly learn, however, is that it's very difficult to talk about gender without talking about society. So we first work through this by airing our anxieties on the topic, and our personal motivations for finding this science interesting. Eventually though, we break into the biology of embryonic sexual differentiation and certain "natural experiments" that alter the course of this differentiation. People with abnormal differentiation offer a chance to see what happens when things like chromosomal sex (XX vs XY) and external genitalia are decoupled, which offers some insight into normal gender development. Next we cover

For most people, the desire to study neuroscience comes from a desire to understand how, in some form, the brain leads to behavior. Generally, neuroscientists focus on the brain side of that relationship, but what obligation do they have to study behavior? Is it even possible to do proper neuroscience without a clear documenting of the behavior we seek to understand? We use a recent opinion article ("Neuroscience Needs Behavior: Correcting a Reductionist Bias") as a jumping off point to discuss these issues. In the paper, the authors argue that behavior is being neglected amongst neuroscientists and it must return to its status as "epistemologically prior." In particular, there are arguments for studying more natural behavior and quantifying behavior more precisely. In this episode we explain our general sympathies with this argument, but question the extent to which change is required. Should all neuroscientists stop what they're doing and study behavior? Are modern technologies drawing scientists away fr

There's a lot to be said about the technical progress being made with artificial intelligence, but what about the impact these rapid advances have on the society in which they unfold? In this episode, we tackle a broad range of such issues, from the possibility of removing human bias from algorithms to how likely we are to fall in love with an AI (Conor might). We speculate on how difficult the transition from humans to self-driving cars will be and our wild uncertainty about the future of jobs/the value of human labor. Throughout you will see a poorly-veiled concern about the current political state of the world and how wealth and power will be distributed in the future. What we learn though, is that in addition to the economic and technological impacts, the use of AI is having at least one major side effect: it's forcing us to explicitly define our goals and values, such that we can impart them to our digital offspring. Now if we could just agree on what those goals and values are...

On this "very special" episode of Unsupervised Thinking, we partner with our fellow podcasters over at Always Already, a critical theory podcast, to burst out of our respective academic bubbles and tackle issues of science and society. The fodder for our conversation is Brain Storm, a book by Rebecca Jordan-Young, that lays out the evidence that prenatal hormone exposures influence gender differences in behavior later in life. In the book, she claims that the sum total of the studies she covers only offers weak support for the hypothesis, and that scientists need to appropriately incorporate other factors into their models such as socialization and environment. While we use this book as a common starting point, our conversation quickly moves beyond the particulars of these gender science studies. We start by questioning who is the intended audience of this book and what it's trying to say to different groups. This moves us into a discussion on critiques of science made by non-scientists and the role that th

In the late 1700s, English minister Thomas Bayes discovered a simple mathematical rule for calculating probabilities based on different information sources. Since then Bayesian models for describing uncertain events have taken off in a wide variety of fields, not the least of which is psychology. This Bayesian framework has been used to understand far-reaching psychological processes, such as how humans combine noisy sensory information with their prior beliefs about the world in order to come to decisions on how to act. But not everyone is riding the Bayesian train. In this episode, we discuss a published back and forth between scientists arguing over the use and merits of Bayesian modeling in neuroscience and psychology. First, though, we set the stage by describing Bayesian math, how it is used in psychology, and the significance of certain terms such as "optimal" (it may not mean what you think it does) and "utility". We then get into the arguments for and against Bayesian modeling, including its falsifi

Computational psychiatry is a field in its infancy, but it offers potential to change the way mental disorders are diagnosed, treated, and understood. In this episode, we cover the different components of computational psychiatry, compare and contrast it to computational neuroscience, and discuss which of its promises are actually likely to be fulfilled. In particular, we get into: -online games that inform diagnoses, -models of learning mechanisms that can explain disorder, -machine learning techniques that advise treatment plans, -neural circuit mechanisms that can('t) explain disease

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 m

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.