Rodolphe Sepulchre is a control engineer and theorist at Cambridge University. He focuses on applying feedback control engineering principles to build circuits that model neurons and neuronal circuits. We discuss his work on mixed feedback control – positive and negative – as an underlying principle of the mixed digital and analog brain signals,, the role of neuromodulation as a controller, applying these principles to Eve Marder’s lobster/crab neural circuits, building mixed-feedback neuromorphics, some feedback control history, and how “If you wish to contribute original work, be prepared to face loneliness,” among other topics
Cameron Buckner is a philosopher and cognitive scientist at The University of Houston. He is writing a book about the age-old philosophical debate on how much of our knowledge is innate (nature, rationalism) versus how much is learned (nurture, empiricism). In the book and his other works, Cameron argues that modern AI can help settle the debate. In particular, he suggests we focus on what types of psychological “domain-general faculties” underlie our own intelligence, and how different kinds of deep learning models are revealing how those faculties may be implemented in our brains. The hope is that by building systems that possess the right handful of faculties, and putting those systems together in a way they can cooperate in a general and flexible manner, it will result in cognitive architectures we would call intelligent. Thus, what Cameron calls The New DoGMA: Domain-General Modular Architecture. We also discuss his work on mental representation and how representations get their content – how our thoughts connect to the natural external world.
Carina Curto is a professor in the Department of Mathematics at The Pennsylvania State University. She uses her background skills in mathematical physics/string theory to study networks of neurons. On this episode, we discuss the world of topology in neuroscience – the study of the geometrical structures mapped out by active populations of neurons. We also discuss her work on “combinatorial linear threshold networks” (CLTNs). Unlike the large deep learning models popular today as models of brain activity, the CLTNs Carina builds are relatively simple, abstracted graphical models. This property is important to Carina, whose goal is to develop mathematically tractable neural network models. Carina has worked out how the structure of many CLTNs allows prediction of the model’s allowable dynamics, how motifs of model structure can be embedded in larger models while retaining their dynamical features, and more. The hope is that these elegant models can tell us more about the principles our messy brains employ to generate the robust and beautiful dynamics underlying our cognition.
Jeff Schall is the director of the Center for Visual Neurophysiology at York University, where he runs the Schall Lab. His research centers around studying the mechanisms of our decisions, choices, movement control, and attention within the saccadic eye movement brain systems and in mathematical psychology models- in other words, how we decide where and when to look. Jeff was my postdoctoral advisor at Vanderbilt University, and I wanted to revisit a few guiding principles he instills in all his students. Linking Propositions by Davida Teller are a series of logical statements to ensure we rigorously connect the brain activity we record to the psychological functions we want to explain. Strong Inference by John Platt is the scientific method on steroids – a way to make our scientific practice most productive and efficient. We discuss both of these topics in the context of Jeff’s eye movement and decision-making science. We also discuss how neurophysiology has changed over the past 30 years, we compare the relatively small models he employs with the huge deep learning models, many of his current projects, and plenty more. If you want to learn more about Jeff’s work and approach, I recommend reading in order two of his review papers we discuss as well. One was written 20 years ago (On Building a Bridge Between Brain and Behavior), and the other 2-ish years ago (Accumulators, Neurons, and Response Time).
Marc Howard runs his Theoretical Cognitive Neuroscience Lab at Boston University, where he develops mathematical models of cognition, constrained by psychological and neural data. In this episode, we discuss the idea that a Laplace transform and its inverse may serve as a unified framework for memory. In short, our memories are compressed on a continuous log-scale: as memories get older, their representations “spread out” in time. It turns out this kind of representation seems ubiquitous in the brain and across cognitive functions, suggesting it is likely a canonical computation our brains use to represent a wide variety of cognitive functions. We also discuss some of the ways Marc is incorporating this mathematical operation in deep learning nets to improve their ability to handle information at different time scales.
