All Episodes
BI 083 Jane Wang: Evolving Altruism in AI
Jane and I discuss the relationship between AI and neuroscience (cognitive science, etc), from her perspective at Deepmind after a career researching natural intelligence. We also talk about her meta-reinforcement learning work that connects deep reinforcement learning with known brain circuitry and processes, and finally we talk about her recent work using evolutionary strategies to develop altruism and cooperation among the agents in a multi-agent reinforcement learning environment.
BI 082 Steve Grossberg: Adaptive Resonance Theory
Steve and I discuss his long and productive career as a theoretical neuroscientist. We cover his tried and true method of taking a large body of psychological behavioral findings, determining how they fit together and what’s paradoxical about them, developing design principles, theories, and models from that body of data, and using experimental neuroscience to inform and confirm his model predictions. We talk about his Adaptive Resonance Theory (ART) to describe how our brains are self-organizing, adaptive, and deal with changing environments. We also talk about his complementary computing paradigm, how the resonant states in ART support consciousness, his place in the history of both neuroscience and AI, and quite a bit more.
BI 081 Pieter Roelfsema: Brain-propagation
Pieter and I discuss his ongoing quest to figure out how the brain implements learning that solves the credit assignment problem, like backpropagation does for neural networks. We also talk about his work to understand how we perceive individual objects in a crowded scene, his neurophysiological recordings in support of the global neuronal workspace hypothesis of consciousness, and the visual prosthetic device he’s developing to cure blindness by directly stimulating early visual cortex.
BI 080 Daeyeol Lee: Birth of Intelligence
Daeyeol and I discuss his book Birth of Intelligence: From RNA to Artificial Intelligence, which argues intelligence is a function of and inseparable from life, bound by self-replication and evolution. The book covers a ton of neuroscience related to decision making and learning, though we focused on a few theoretical frameworks and ideas like division of labor and principal-agent relationships to understand how our brains and minds are related to our genes, how AI is related to humans (for now), metacognition, consciousness, and a ton more.
BI 079 Romain Brette: The Coding Brain Metaphor
Romain and I discuss his theoretical/philosophical work examining how neuroscientists rampantly misuse the word “code” when making claims about information processing in brains. We talk about the coding metaphor, various notions of information, the different roles and facets of mental representation, perceptual invariance, subjective physics, process versus substance metaphysics, and the process of writing a Behavior and Brain Sciences article (spoiler: it’s a demanding yet rewarding experience).
BI 078 David and John Krakauer: Part 2
In this second part of our conversation David, John, and I continue to discuss the role of complexity science in the study of intelligence, brains, and minds. Be sure to listen to the first part, which lays the foundation for what we discuss in this episode.
BI 077 David and John Krakauer: Part 1
David, John, and I discuss the role of complexity science in the study of intelligence. In this first part, we talk about complexity itself, its role in neuroscience, emergence and levels of explanation, understanding, and really quite a bit more.
BI 076 Olaf Sporns: Network Neuroscience
Olaf and I discuss the explosion of network neuroscience, which uses network science tools to map the structure (connectome) and activity of the brain at various spatial and temporal scales. We talk about the possibility of bridging physical and functional maps via communication dynamics, and about the relation between network science and artificial neural networks and plenty more.
BI 075 Jim DiCarlo: Reverse Engineering Vision
Jim and I discuss his reverse engineering approach to visual intelligence, using deep models optimized to perform object recognition tasks. We talk about the history of his work developing models to match the neural activity in the ventral visual stream, how deep learning connects with those models, and some of his recent work: adding recurrence to the models to account for more difficult object recognition, using unsupervised learning to account for plasticity in the visual stream, and controlling neural activity by creating specific images for subjects to view.
BI 074 Ginger Campbell: Are You Sure?
Ginger and I discuss her book Are You Sure? The Unconscious Origins of Certainty, which summarizes Richard Burton’s work exploring the experience and phenomenal origin of feeling confident, and how the vast majority of our brain processing occurs outside our conscious awareness.
BI 073 Megan Peters: Consciousness and Metacognition
Megan and I discuss her work using metacognition as a way to study subjective awareness, or confidence. We talk about how our decisions are related to our confidence, the current state of the science of consciousness, and her newest project using fMRI decoded neurofeedback to induce particular brain states in subjects so we can learn about conscious and unconscious brain processing.
BI 072 Mazviita Chirimuuta: Understanding, Prediction, and Reality
Mazviita and I discuss the growing divide between prediction and understanding as neuroscience models and deep learning networks become bigger and more complex. She describes her non-factive account of understanding, which among other things suggests that the best predictive models may deliver less understanding. We also discuss the brain as a computer metaphor, and whether it’s really possible to ignore all the traditionally “non-computational” parts of the brain like metabolism and other life processes.
BI 071 J. Patrick Mayo: The Path To Faculty
Patrick and I mostly discuss his path from a technician in the then nascent Jim DiCarlo lab, through his graduate school and two postdoc experiences, and finally landing a faculty position, plus the culture and issues in academia in general. We also cover plenty of science, like the role of eye movements in the study of vision, the neuroscience (and concept of) attention, what Patrick thinks of the deep learning hype, and more.
BI 070 Bradley Love: How We Learn Concepts
Brad and I discuss his battle-tested, age-defying cognitive model for how we learn concepts by forming and rearranging clusters, how the model maps onto brain areas, and how he’s using deep learning models to explore how attention and sensory information interact with concept formation. We also discuss the cognitive modeling approach, Marr’s levels of analysis, the term “biological plausibility”, emergence and reduction, and plenty more.
BI 069 David Ferrucci: Machines To Understand Stories
David and I discuss the latest efforts he and his Elemental Cognition team have made to create machines that can understand stories the way humans can and do. The long term vision is to create what David calls “thought partners”, which are virtual assistants that can learn and synthesize a massive amount of information for us when we need that information for whatever project we’re working on. We also discuss the nature of understanding, language, the role of the biological sciences for AI, and more.
BI 068 Rodrigo Quian Quiroga: NeuroScience Fiction
Rodrigo and I discuss concept cells and his latest book, NeuroScience Fiction. The book is a whirlwind of many of the big questions in neuroscience, each one framed by of one of Rodrigo’s favorite science fiction films and buttressed by tons of history, literature, and philosophy. We discuss a few of the topics in the book, like AI, identity, free will, consciousness, and immortality, and we keep returning to concept cells and the role of abstraction in human cognition.
BI 067 Paul Cisek: Backward Through The Brain
In this second part of my conversion with Paul, we continue our discussion about how to understand brains as feedback control mechanisms – controlling our internal state and extending that control into the world – and how Paul thinks the key to understanding intelligence is to trace our evolutionary past through phylogenetic refinement.
BI 066 Paul Cisek: Forward Through Evolution
In this first part of our conversation, Paul and I discuss his approach to understanding how the brain (and intelligence) works. Namely, he believes we are fundamentally action and movement oriented – all of our behavior and cognition is based on controlling ourselves and our environment through feedback control mechanisms, and basically all neural activity should be understood through that lens. This contrasts with the view that we serially perceive the environment, make internal representations of what we perceive, do some cognition on those representations, and transform that cognition into decisions about how to move. From that premise, Paul also believes the best (and perhaps only) way to understand our current brains is by tracing out the evolutionary steps that took us from our single celled first organisms all the way to us – a process he calls phylogenetic refinement.