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, epistemology and ontology, and really quite a bit more.

Notes:

• David’s page at the Santa Fe Institute.
• John’s BLAM lab website.
• Follow SFI on twitter: @sfiscience.
• BLAM on Twitter: @blamlab 
• Related Krakauer stuff:
  • At the limits of thought. An Aeon article by David
  • Complex Time: Cognitive Regime Shift II - When/Why/How the Brain Breaks. A video conversation with both John and David.
  • Complexity Podcast.
• Books mentioned:
  • Worlds Hidden in Plain Sight: The Evolving Idea of Complexity at the Santa Fe Institute, ed. David Krakauer.
  • Understanding Scientific Understanding by Henk de Regt.
  • The Idea of the Brain by Matthew Cobb.
  • New Dark Age: Technology and the End of the Future by James Bridle.
  • The River of Consciousness by Oliver Sacks.

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 connectivity via communication dynamics, and about the relation between network science and artificial neural networks and plenty more.

Notes:

• Computational Cognitive Neuroscience Laboratory.
• Twitter: @spornslab
• His excellent book: Networks of the Brain.
• Related papers:
  • Network Neuroscience.
  • The economy of brain network organization.
  • Communication dynamics in complex brain networks.

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.

Notes:

• The DiCarlo Lab at MIT.
• Related papers:
  • Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks.
  • Fast recurrent processing via ventral prefrontal cortex is needed by the primate ventral stream for robust core visual object recognition.
  • Unsupervised changes in core object recognition behavioral performance are accurately predicted by unsupervised neural plasticity in inferior temporal cortex.
  • Neural population control via deep image synthesis.

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.

• Are You Sure? The Unconscious Origins of Certainty.
• Brain Science Podcast.

Megan and I discuss her work using metacognition as a way to study subjective awareness, or confidence. We talk about using computational and neural network models to probe how 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.

Notes:

• Visit Megan's cognitive & neural computation lab.
• Twitter: @meganakpeters
• The papers we discuss or mention:
  • Human intracranial electrophysiology suggests suboptimal calculations underlie perceptual confidence
  • Tuned normalization in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior.

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.

Show notes:

• Her website.
• Outside color website (with links to more of her publications)
• Her book Outside Color: Perceptual Science and the Puzzle of Color in Philosophy.
• Papers we discuss or mention:
  • Prediction Versus Understanding in Computationally Enhanced Neuroscience.
  • Your brain is like a computer: function, analogy, simplification.
  • Charting the Heraclitean Brain: Perspectivism and Simplification in Models of the Motor Cortex.

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.

But, this is a special episode, less about the science and more about the experience of an academic neuroscience trajectory/life. Episodes like this will appear in Patreon supporters' private feeds from now on.

Show notes:

• His pre-lab website university page.
• Twitter: @mayo_lab.
• Here’s the paper he recommends to understand attention:
  • Attention can be subdivided into neurobiological components corresponding to distinct behavioral effects.

Brad and I discuss his battle-tested, age-defying cognitive model for how we learn and store 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.

Notes:

• Visit Brad’s website.
• Follow Brad on twitter: @ProfData.
• Related papers:
  • Levels of Biological Plausibility.
  • Models in search of a brain.
  • A non-spatial account of place and grid cells based on clustering models of concept learning.
  • Abstract neural representations of category membership beyond information coding stimulus or response.
  • Ventromedial prefrontal cortex compression during concept learning.
  • The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks
  • Learning as the unsupervised alignment of conceptual systems.

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.

• Dave’s business Elemental Cognition.
• The paper we discuss:
  • To Test Machine Comprehension, Start by Defining Comprehension.

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.

Notes:

• Rodrigo's lab website: Centre for Systems Neuroscience at the University of Leicester, UK
• His book:
  • NeuroScience Fiction: From "2001: A Space Odyssey" to "Inception," How Neuroscience Is Transforming Sci-Fi into Reality―While Challenging Our Beliefs About the Mind, Machines, and What Makes us Human.
• Papers we discuss or mention:
  • Concept cells: the building blocks of declarative memory functions.
  • Neural representations across species.
  • Searching for the neural correlates of human intelligence.
• Talks:
  • Concept cells and their role in memory - Part 1 and Part 2